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Computer vision (CV)-based methods have the advantages of being low cost, simple to operate, remote, and non-contact, and have been widely used in bridge inspection and monitoring in recent years. Therefore, this paper reviews three significant aspects of CV-based methods, including surface defect detection, vibration measurement, and vehicle parameter identification. Firstly, the general procedure for CV-based surface defect detection is introduced, and its application for the detection of cracks, concrete spalling, steel corrosion, and multi-defects is reviewed, followed by the robot platforms for surface defect detection. Secondly, the basic principle of CV-based vibration measurement is introduced, followed by the application of displacement measurement, modal identification, and damage identification. Finally, the CV-based vehicle parameter identification methods are introduced and their application for the identification of temporal and spatial parameters, weight parameters, and multi-parameters are summarized. This comprehensive literature review aims to provide guidance for selecting appropriate CV-based methods for bridge inspection and monitoring.<\/jats:p>","DOI":"10.3390\/s23187863","type":"journal-article","created":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T10:09:22Z","timestamp":1694686162000},"page":"7863","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":125,"title":["Computer Vision-Based Bridge Inspection and Monitoring: A Review"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0967-1156","authenticated-orcid":false,"given":"Kui","family":"Luo","sequence":"first","affiliation":[{"name":"College of Civil Engineering, Hunan University, Changsha 410082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5919-1970","authenticated-orcid":false,"given":"Xuan","family":"Kong","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Hunan University, Changsha 410082, China"},{"name":"Key Laboratory for Damage Diagnosis of Engineering Structures of Hunan Province, College of Civil Engineering, Hunan University, Changsha 410082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5579-9620","authenticated-orcid":false,"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Hunan University, Changsha 410082, China"}]},{"given":"Jiexuan","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Hunan University, Changsha 410082, China"}]},{"given":"Jinzhao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Hunan University, Changsha 410082, China"}]},{"given":"Hao","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Hunan University, Changsha 410082, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1007\/s13349-020-00431-2","article-title":"A portable monitoring approach using cameras and computer vision for bridge load rating in smart cities","volume":"10","author":"Dong","year":"2020","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kim, I., Jung, H., Yoon, S., and Park, J.W. (2023). Dynamic Response Measurement and Cable Tension Estimation Using an Unmanned Aerial Vehicle. Remote Sens., 15.","DOI":"10.3390\/rs15164000"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.eng.2018.11.030","article-title":"Advances in computer vision-based civil infrastructure inspection and monitoring","volume":"5","author":"Spencer","year":"2019","journal-title":"Engineering"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"115741","DOI":"10.1016\/j.jsv.2020.115741","article-title":"Review on the new development of vibration-based damage identification for civil engineering structures: 2010\u20132019","volume":"491","author":"Hou","year":"2021","journal-title":"J. Sound Vib."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kao, S.P., Chang, Y.C., and Wang, F.L. (2023). Combining the YOLOv4 deep learning model with UAV imagery processing technology in the extraction and quantization of cracks in bridges. Sensors, 23.","DOI":"10.3390\/s23052572"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107077","DOI":"10.1016\/j.ymssp.2020.107077","article-title":"A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications","volume":"147","author":"Avci","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.eswa.2017.09.033","article-title":"Survey of computer vision algorithms and applications for unmanned aerial vehicles","volume":"92","author":"Martin","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"04020028","DOI":"10.1061\/(ASCE)CP.1943-5487.0000907","article-title":"Deep learning-based enhancement of motion blurred UAV concrete crack images","volume":"34","author":"Liu","year":"2020","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xu, Z., Wang, Y., Hao, X., and Fan, J. (2023). Crack Detection of Bridge Concrete Components Based on Large-Scene Images Using an Unmanned Aerial Vehicle. Sensors, 23.","DOI":"10.3390\/s23146271"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kim, B., Choi, S.W., Hu, G., Lee, D.E., and Serfa Juan, R.O. (2022). An Automated Image-Based Multivariant Concrete Defect Recognition Using a Convolutional Neural Network with an Integrated Pooling Module. Sensors, 22.","DOI":"10.3390\/s22093118"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.engstruct.2017.10.057","article-title":"Visual data classification in post-event building reconnaissance","volume":"155","author":"Yeum","year":"2018","journal-title":"Eng. Struct."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Khan, M.A.M., Kee, S.H., Pathan, A.S.K., and Nahid, A.A. (2023). Image Processing Techniques for Concrete Crack Detection: A Scien-tometrics Literature Review. Remote Sens., 15.","DOI":"10.3390\/rs15092400"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhou, M., Lu, W., Xia, J., and Wang, Y. (2023). Defect Detection in Steel Using a Hybrid Attention Network. Sensors, 23.","DOI":"10.3390\/s23156982"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, C., Chen, Y., Tang, L., Chu, X., and Li, C. (2023). CTCD-Net: A Cross-Layer Transmission Network for Tiny Road Crack Detection. Remote Sens., 15.","DOI":"10.3390\/rs15082185"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, I.H., Jeon, H., Baek, S.C., Hong, W.H., and Jung, H.J. (2018). Application of crack identification techniques for an aging concrete bridge inspection using an unmanned aerial vehicle. Sensors, 18.","DOI":"10.3390\/s18061881"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"110648","DOI":"10.1016\/j.enbuild.2020.110648","article-title":"Building envelope modeling calibration using aerial thermography","volume":"233","author":"Bayomi","year":"2021","journal-title":"Energy Build."},{"key":"ref_17","first-page":"8640674","article-title":"A laser-based noncontact vibration technique for health monitoring of structural cables: Background, success, and new developments","volume":"2018","author":"Mehrabi","year":"2018","journal-title":"Adv. Acous. Vib."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"112728","DOI":"10.1016\/j.engstruct.2021.112728","article-title":"Noncontact operational modal analysis of light poles by vision-based motion-magnification method","volume":"244","author":"Siringoringo","year":"2021","journal-title":"Eng. Struct."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7103039","DOI":"10.1155\/2016\/7103039","article-title":"A review of machine vision-based structural health monitoring: Methodologies and applications","volume":"2016","author":"Ye","year":"2016","journal-title":"J. Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.jsv.2017.06.008","article-title":"Identification of structural stiffness and excitation forces in time domain using noncontact vision-based displacement measurement","volume":"406","author":"Feng","year":"2017","journal-title":"J. Sound Vib."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.measurement.2016.12.020","article-title":"Cable tension force estimate using novel noncontact vision-based sensor","volume":"99","author":"Feng","year":"2017","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"110392","DOI":"10.1016\/j.ymssp.2023.110392","article-title":"Target-free recognition of cable vibration in complex backgrounds based on computer vision","volume":"197","author":"Wang","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"110617","DOI":"10.1016\/j.ymssp.2023.110617","article-title":"Fast and robust vision-based cable force monitoring method free from environmental disturbances","volume":"201","author":"Yu","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103224","DOI":"10.1016\/j.autcon.2020.103224","article-title":"Condition monitoring of bridges with non-contact testing technologies","volume":"116","author":"Dabous","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113091","DOI":"10.1016\/j.measurement.2023.113091","article-title":"Semi-supervised learning framework for crack segmentation based on contrastive learning and cross pseudo supervision","volume":"217","author":"Xiang","year":"2023","journal-title":"Measurement"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104894","DOI":"10.1016\/j.autcon.2023.104894","article-title":"A crack-segmentation algorithm fusing transformers and convolutional neural networks for complex detection scenarios","volume":"152","author":"Xiang","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"115809","DOI":"10.1016\/j.engstruct.2023.115809","article-title":"A robust bridge rivet identification method using deep learning and computer vision","volume":"283","author":"Jiang","year":"2023","journal-title":"Eng. Struct."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"114994","DOI":"10.1016\/j.engstruct.2022.114994","article-title":"Completely non-contact modal testing of full-scale bridge in challenging conditions using vision sensing systems","volume":"272","author":"Wang","year":"2022","journal-title":"Eng. Struct."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"116875","DOI":"10.1016\/j.jsv.2022.116875","article-title":"A detailed investigation of uplift and damping of a railway catenary span in traffic using a vision-based line-tracking system","volume":"527","author":"Jiang","year":"2022","journal-title":"J. Sound Vib."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106888","DOI":"10.1016\/j.ymssp.2020.106888","article-title":"A robust line-tracking photogrammetry method for uplift measurements of railway catenary systems in noisy backgrounds","volume":"144","author":"Jiang","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kong, X., Wang, T., Zhang, J., Deng, L., Zhong, J., Cui, Y., and Xia, S. (2022). Tire contact force equations for vision-based vehicle weight identification. Appl. Sci., 12.","DOI":"10.3390\/app12094487"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109093","DOI":"10.1016\/j.ymssp.2022.109093","article-title":"Non-contact vehicle weighing method based on tire-road contact model and computer vision techniques","volume":"174","author":"Kong","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"104678","DOI":"10.1016\/j.autcon.2022.104678","article-title":"Deep learning-based crack segmentation for civil infrastructure: Data types, architectures, and benchmarked performance","volume":"146","author":"Zhou","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"129238","DOI":"10.1016\/j.conbuildmat.2022.129238","article-title":"Review on computer vision-based crack detection and quantification methodologies for civil structures","volume":"356","author":"Deng","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"04020113","DOI":"10.1061\/(ASCE)CF.1943-5509.0001519","article-title":"Literature review and technical survey on bridge inspection using unmanned aerial vehicles","volume":"34","author":"Jeong","year":"2020","journal-title":"J. Perform. Constr. Facil."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"112382","DOI":"10.1016\/j.measurement.2022.112382","article-title":"Review of robot-based automated measurement of vibration for civil engineering structures","volume":"207","author":"Poorghasem","year":"2022","journal-title":"Measurement"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhuang, Y., Chen, W., Jin, T., Chen, B., Zhang, H., and Zhang, W. (2022). A review of computer vision-based structural deformation monitoring in field environments. Sensors, 22.","DOI":"10.3390\/s22103789"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yoon, H., Hoskere, V., Park, J.W., and Spencer, B.F. (2017). Cross-correlation-based structural system identification using unmanned aerial vehicles. Sensors, 17.","DOI":"10.3390\/s17092075"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105022","DOI":"10.1016\/j.autcon.2023.105022","article-title":"Online monitoring of crack dynamic development using attention-based deep networks","volume":"154","author":"Chen","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1177\/0361198118780825","article-title":"Field application of UAS-based bridge inspection","volume":"2672","author":"Seo","year":"2018","journal-title":"Transport Res. Rec."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"105478","DOI":"10.1016\/j.engappai.2022.105478","article-title":"Computer vision framework for crack detection of civil infrastructure\u2014A review","volume":"117","author":"Ai","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, Q., and Liu, X. (2008, January 27\u201330). Novel approach to pavement image segmentation based on neighboring difference histogram method. Proceedings of the 2008 Congress on Image and Signal Processing, Sanya, China.","DOI":"10.1109\/CISP.2008.13"},{"key":"ref_44","unstructured":"Lim, R.S., La, H.M., Shan, Z., and Sheng, W. (2011, January 9\u201313). Developing a crack inspection robot for bridge maintenance. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhao, H., Qin, G., and Wang, X. (2010, January 16\u201318). Improvement of canny algorithm based on pavement edge detection. Proceedings of the 2010 3rd International Congress on Image and Signal Processing, Yantai, China.","DOI":"10.1109\/CISP.2010.5646923"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.imavis.2011.10.003","article-title":"FoSA: F* seed-growing approach for crack-line detection from pavement images","volume":"29","author":"Li","year":"2011","journal-title":"Image Vision Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"04020038","DOI":"10.1061\/(ASCE)CP.1943-5487.0000918","article-title":"Machine learning for crack detection: Review and model performance comparison","volume":"34","author":"Hsieh","year":"2020","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Sapijaszko, G., and Mikhael, W.B. (2018, January 5\u20138). An overview of recent convolutional neural network algorithms for image recognition. Proceedings of the 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS), Windsor, ON, Canada.","DOI":"10.1109\/MWSCAS.2018.8623911"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 8\u201310). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1111\/mice.12440","article-title":"Encoder-decoder network for pixel-level road crack detection in black-box images","volume":"34","author":"Bang","year":"2019","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_52","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"109454","DOI":"10.1016\/j.measurement.2021.109454","article-title":"Development of a YOLO-V3-based model for detecting defects on steel strip surface","volume":"182","author":"Kou","year":"2021","journal-title":"Measurement"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"8205","DOI":"10.1016\/j.ifacol.2020.12.1994","article-title":"On bridge surface crack detection based on an improved YOLO v3 algorithm","volume":"53","author":"Zhang","year":"2020","journal-title":"IFAC-PapersOnLine"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Long","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Schmugge, S.J., Rice, L., Nguyen, N.R., Lindberg, J., Grizzi, R., Joffe, C., and Shin, M.C. (2016, January 7\u201310). Detection of cracks in nuclear power plant using spatial-temporal grouping of local patches. Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477601"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.autcon.2018.11.028","article-title":"Autonomous concrete crack detection using deep fully convolutional neural network","volume":"99","author":"Dung","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s11831-018-9263-6","article-title":"A brief review and a new graph-based image analysis for concrete crack quantification","volume":"26","author":"Payab","year":"2019","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.autcon.2013.06.011","article-title":"Image-based retrieval of concrete crack properties for bridge inspection","volume":"39","author":"Adhikari","year":"2014","journal-title":"Autom. Constr."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1177\/1475921720935585","article-title":"A review of computer vision-based structural health monitoring at local and global levels","volume":"20","author":"Dong","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"129659","DOI":"10.1016\/j.conbuildmat.2022.129659","article-title":"Automatic bridge crack detection using Unmanned aerial vehicle and Faster R-CNN","volume":"362","author":"Li","year":"2023","journal-title":"Constr. Build. Mater."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1864","DOI":"10.1177\/1475921720940068","article-title":"A research on an improved Unet-based concrete crack detection algorithm","volume":"20","author":"Zhang","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"104346","DOI":"10.1016\/j.autcon.2022.104346","article-title":"Crack detection algorithm for concrete structures based on super-resolution reconstruction and segmentation network","volume":"140","author":"Xiang","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"103786","DOI":"10.1016\/j.autcon.2021.103786","article-title":"Semi-supervised semantic segmentation network for surface crack detection","volume":"128","author":"Wang","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1111\/mice.12881","article-title":"Tiny-Crack-Net: A multiscale feature fusion network with attention mechanisms for segmentation of tiny cracks","volume":"37","author":"Chu","year":"2022","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Xu, H., Su, X., Wang, Y., Cai, H., Cui, K., and Chen, X. (2019). Automatic bridge crack detection using a convolutional neural network. Appl. Sci., 9.","DOI":"10.3390\/app9142867"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yang, F., Zhang, Y.D., and Zhu, Y.J. (2016, January 25\u201328). Road crack detection using deep convolutional neural network. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533052"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1016\/j.dib.2018.11.015","article-title":"SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks","volume":"21","author":"Dorafshan","year":"2018","journal-title":"Data Brief"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.neucom.2019.01.036","article-title":"DeepCrack: A deep hierarchical feature learning architecture for crack segmentation","volume":"338","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1111\/0031-868X.00198","article-title":"An operational application of automatic feature extraction: The measurement of cracks in concrete structures","volume":"17","author":"Dare","year":"2002","journal-title":"Photogramm. Rec."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s00138-009-0244-5","article-title":"A robust automatic crack detection method from noisy concrete surfaces","volume":"22","author":"Fujita","year":"2011","journal-title":"Mach. Vision Appl."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.conbuildmat.2019.01.150","article-title":"A fast adaptive crack detection algorithm based on a double-edge extraction operator of FSM","volume":"204","author":"Luo","year":"2019","journal-title":"Constr. Build. Mater."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Kim, H., Lee, J., Ahn, E., Cho, S., Shin, M., and Sim, S.H. (2017). Concrete crack identification using a UAV incorporating hybrid image processing. Sensors, 17.","DOI":"10.3390\/s17092052"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"103781","DOI":"10.1016\/j.cemconcomp.2020.103781","article-title":"Classification and quantification of cracks in concrete structures using deep learning image-based techniques","volume":"114","author":"Flah","year":"2020","journal-title":"Cem. Concr. Compos."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"103535","DOI":"10.1016\/j.autcon.2020.103535","article-title":"Robust pixel-wise concrete crack segmentation and properties retrieval using image patches","volume":"123","author":"Liu","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1111\/mice.12667","article-title":"Pixel-level multicategory detection of visible seismic damage of reinforced concrete components","volume":"36","author":"Miao","year":"2021","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"035019","DOI":"10.1088\/0964-1726\/22\/3\/035019","article-title":"A new methodology for non-contact accurate crack width measurement through photogrammetry for automated structural safety evaluation","volume":"22","author":"Jahanshahi","year":"2013","journal-title":"Smart Mater. Struct."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"103031","DOI":"10.1016\/j.advengsoft.2021.103031","article-title":"Automatic recognition of concrete spall using image processing and metaheuristic optimized LogitBoost classification tree","volume":"159","author":"Cao","year":"2021","journal-title":"Adv. Eng. Softw."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"104324","DOI":"10.1016\/j.autcon.2022.104324","article-title":"Detection of exposed steel rebars based on deep-learning techniques and unmanned aerial vehicles","volume":"139","author":"Santos","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"8829715","DOI":"10.1155\/2020\/8829715","article-title":"Image processing-based spall object detection using gabor filter, texture analysis, and adaptive moment estimation (Adam) optimized logistic regression models","volume":"2020","author":"Hoang","year":"2020","journal-title":"Adv. Civ. Eng."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"5551555","DOI":"10.1155\/2021\/5551555","article-title":"Concrete spalling severity classification using image texture analysis and a novel jellyfish search optimized machine learning approach","volume":"2021","author":"Hoang","year":"2021","journal-title":"Adv. Civ. Eng."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"104371","DOI":"10.1016\/j.autcon.2022.104371","article-title":"Computer vision-based classification of concrete spall severity using metaheuristic-optimized extreme gradient boosting machine and deep convolutional neural network","volume":"140","author":"Nguyen","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"04020132","DOI":"10.1061\/(ASCE)CF.1943-5509.0001544","article-title":"Entropy-based automated method for detection and assessment of spalling severities in reinforced concrete bridges","volume":"35","author":"Mohammed","year":"2021","journal-title":"J. Perform. Constr. Facil."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"116461","DOI":"10.1016\/j.eswa.2021.116461","article-title":"CorrDetector: A framework for structural corrosion detection from drone images using ensemble deep learning","volume":"193","author":"Forkan","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"125877","DOI":"10.1016\/j.conbuildmat.2021.125877","article-title":"Corrosion detection and evaluation for steel wires based on a multi-vision scanning system","volume":"322","author":"Dong","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"103382","DOI":"10.1016\/j.autcon.2020.103382","article-title":"Inspection of surface defects on stay cables using a robot and transfer learning","volume":"119","author":"Hou","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"107843","DOI":"10.1016\/j.measurement.2020.107843","article-title":"Surface damage detection for steel wire ropes using deep learning and computer vision techniques","volume":"161","author":"Huang","year":"2020","journal-title":"Measurement"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"100022","DOI":"10.1016\/j.dibe.2020.100022","article-title":"Detection of corrosion on steel structures using automated image processing","volume":"3","author":"Khayatazad","year":"2020","journal-title":"Dev. Built Environ."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"04022059","DOI":"10.1061\/(ASCE)CF.1943-5509.0001773","article-title":"A Detection Method for Bridge Cables Based on Intelligent Image Recognition and Magnetic-Memory Technology","volume":"36","author":"Meng","year":"2022","journal-title":"J. Perform. Constr. Facil."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"e3079","DOI":"10.1002\/stc.3079","article-title":"Multiclass damage detection in concrete structures using a transfer learning-based generative adversarial networks","volume":"29","author":"Dunphy","year":"2022","journal-title":"Struct. Control Health Monit."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"102824","DOI":"10.1016\/j.autcon.2019.04.019","article-title":"Multi-classifier for reinforced concrete bridge defects","volume":"105","author":"Lu","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1111\/mice.12334","article-title":"Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types","volume":"33","author":"Cha","year":"2018","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Kim, B., and Cho, S. (2020). Automated multiple concrete damage detection using instance segmentation deep learning model. Appl. Sci., 10.","DOI":"10.3390\/app10228008"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1111\/mice.12500","article-title":"Concrete bridge surface damage detection using a single-stage detector","volume":"35","author":"Zhang","year":"2020","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1111\/mice.12433","article-title":"Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network","volume":"34","author":"Li","year":"2019","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"103831","DOI":"10.1016\/j.autcon.2021.103831","article-title":"Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures","volume":"130","author":"Ali","year":"2021","journal-title":"Automt. Constr."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"123896","DOI":"10.1016\/j.conbuildmat.2021.123896","article-title":"A UAV-based machine vision method for bridge crack recognition and width quantification through hybrid feature learning","volume":"299","author":"Peng","year":"2021","journal-title":"Constr. Build. Mater."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"La, H., Gucunski, N., Kee, S., Yi, J., Senlet, T., and Nguyen, L. (2014, January 14\u201318). Autonomous robotic system for bridge deck data collection and analysis. Proceedings of the 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6942821"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.autcon.2018.02.021","article-title":"Automatic multi-image stitching for concrete bridge inspection by combining point and line features","volume":"90","author":"Xie","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Leibbrandt, A., Caprari, G., Angst, U., Siegwart, R.Y., Flatt, R.J., and Elsener, B. (2012, January 11\u201313). Climbing robot for corrosion monitoring of reinforced concrete structures. Proceedings of the 2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI), Zurich, Switzerland.","DOI":"10.1109\/CARPI.2012.6473365"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Guan, D., Yan, L., Yang, Y., and Xu, W. (2014, January 26\u201328). A small climbing robot for the intelligent inspection of nuclear power plants. Proceedings of the 2014 4th IEEE International Conference on Information Science and Technology, Shenzhen, China.","DOI":"10.1109\/ICIST.2014.6920522"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Jung, S., Song, S., Kim, S., Park, J., Her, J., Roh, K., and Myung, H. (2019, January 24\u201327). Toward Autonomous Bridge Inspection: A framework and experimental results. Proceedings of the 2019 16th International Conference on Ubiquitous Robots (UR), Jeju, Republic of Korea.","DOI":"10.1109\/URAI.2019.8768677"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Liu, Q., and Liu, Y. (2013, January 12\u201314). An approach for auto bridge inspection based on climbing robot. Proceedings of the 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), Shenzhen, China.","DOI":"10.1109\/ROBIO.2013.6739861"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1111\/mice.12550","article-title":"Automated crack evaluation of a high-rise bridge pier using a ring-type climbing robot","volume":"36","author":"Jang","year":"2021","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1017\/S0263574719001814","article-title":"Design, modeling, and control of a new manipulating climbing robot through infrastructures using adaptive force control method","volume":"38","author":"Boomeri","year":"2020","journal-title":"Robotica"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.autcon.2018.02.013","article-title":"A semi-autonomous mobile robot for bridge inspection","volume":"91","author":"Sutter","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Liu, Y., Dai, Q., and Liu, Q. (2013, January 26\u201329). Adhesion-adaptive control of a novel bridge-climbing robot. Proceedings of the 2013 International Conference on Cyber Technology in Automation, Control and Intelligent Systems, Nanjing, China.","DOI":"10.1109\/CYBER.2013.6705428"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.autcon.2018.07.003","article-title":"Localisation of a mobile robot for bridge bearing inspection","volume":"94","author":"Peel","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s10846-020-01266-1","article-title":"A climbing robot for steel bridge inspection","volume":"102","author":"Nguyen","year":"2021","journal-title":"J. Intell. Robot Syst."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s12206-011-1234-x","article-title":"Inspection method of cable-stayed bridge using magnetic flux leakage detection: Principle, sensor design, and signal processing","volume":"26","author":"Xu","year":"2012","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"967508","DOI":"10.1155\/2013\/967508","article-title":"Development of inspection robots for bridge cables","volume":"2013","author":"Yun","year":"2013","journal-title":"Sci. World J."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1109\/TMECH.2016.2614578","article-title":"Multifunctional robotic crawler for inspection of suspension bridge hanger cables: Mechanism design and performance validation","volume":"22","author":"Cho","year":"2016","journal-title":"IEEE-ASME Trans. Mech."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1111\/mice.12375","article-title":"Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo-tagging","volume":"33","author":"Kang","year":"2018","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.autcon.2018.06.006","article-title":"Drone-enabled bridge inspection methodology and application","volume":"94","author":"Seo","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.autcon.2016.08.024","article-title":"Bridge deck delamination identification from unmanned aerial vehicle infrared imagery","volume":"72","author":"Ellenberg","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Sanchez-Cuevas, P.J., Ramon-Soria, P., Arrue, B., Ollero, A., and Heredia, G. (2019). Robotic system for inspection by contact of bridge beams using UAVs. Sensors, 19.","DOI":"10.3390\/s19020305"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1111\/mice.12519","article-title":"Real-time crack assessment using deep neural networks with wall-climbing unmanned aerial system","volume":"35","author":"Jiang","year":"2020","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"111224","DOI":"10.1016\/j.engstruct.2020.111224","article-title":"Investigation of vibration serviceability of a footbridge using computer vision-based methods","volume":"224","author":"Dong","year":"2020","journal-title":"Eng. Struct."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"108449","DOI":"10.1016\/j.measurement.2020.108449","article-title":"A smartphone camera and built-in gyroscope based application for non-contact yet accurate off-axis structural displacement measurements","volume":"167","author":"Yu","year":"2021","journal-title":"Measurement"},{"key":"ref_125","unstructured":"Hartley, R., and Zisserman, A. (2001). Multiple View Geometry in Computer Vision, Emerald Group Publishing Limited."},{"key":"ref_126","unstructured":"Smith, W.J. (2008). Modern Optical Engineering: The Design of Optical Systems, McGraw-Hill Education. [4th ed.]. Available online: https:\/\/www.accessengineeringlibrary.com\/content\/book\/9780071476874."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1080\/15732479.2019.1650078","article-title":"Structural displacement monitoring using deep learning-based full field optical flow methods","volume":"16","author":"Dong","year":"2020","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.measurement.2017.09.043","article-title":"Identification of structural dynamic characteristics based on machine vision technology","volume":"126","author":"Dong","year":"2018","journal-title":"Measurement"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"e1852","DOI":"10.1002\/stc.1852","article-title":"Completely contactless structural health monitoring of real-life structures using cameras and computer vision","volume":"24","author":"Khuc","year":"2017","journal-title":"Struct. Control Health Monit."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1177\/1475921718806895","article-title":"Marker-free monitoring of the grandstand structures and modal identification using computer vision methods","volume":"18","author":"Dong","year":"2019","journal-title":"Struct. Health Monit."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.compstruc.2018.02.001","article-title":"A computer vision approach for the load time history estimation of lively individuals and crowds","volume":"200","author":"Celik","year":"2018","journal-title":"Comput. Struct."},{"key":"ref_132","first-page":"123","article-title":"Development and evaluation of a long range image-based monitoring system for civil engineering structures","volume":"9437","author":"Ehrhart","year":"2015","journal-title":"Struct. Health Monit. Insp. Adv. Mater. Aerosp. Civ. Infrastruct."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"935","DOI":"10.12989\/sss.2016.17.6.935","article-title":"Image-based structural dynamic displacement measurement using different multi-object tracking algorithms","volume":"17","author":"Ye","year":"2016","journal-title":"Smart Struct. Syst."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., and Torr, P.H. (15\u201316, January 8\u201310). Fully-convolutional siamese networks for object tracking. Proceedings of the Computer Vision-ECCV 2016 Workshops, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhang, L., Liu, Q., Zhang, D., and Yang, M.H. (2014, January 6\u201312). Fast visual tracking via dense spatio-temporal context learning. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_9"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vision"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"e2009","DOI":"10.1002\/stc.2009","article-title":"Pixel-wise structural motion tracking from rectified repurposed videos","volume":"24","author":"Khaloo","year":"2017","journal-title":"Struct. Control Health Monit."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Tian, T.Y., Tomasi, C., and Heeger, D.J. (1996, January 18\u201320). Comparison of approaches to egomotion computation. Proceedings of the CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.1996.517091"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1111\/mice.12390","article-title":"Rapid impact testing and system identification of footbridges using particle image velocimetry","volume":"34","author":"Tian","year":"2019","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, L., Bertinetto, L., Hu, W., and Torr, P.H. (2019, January 15\u201321). Fast online object tracking and segmentation: A unifying approach. Proceedings of the Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition, Los Angeles, CA, USA.","DOI":"10.1109\/CVPR.2019.00142"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"e2155","DOI":"10.1002\/stc.2155","article-title":"A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge","volume":"25","author":"Xu","year":"2018","journal-title":"Struct. Health Monit."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"05018014","DOI":"10.1061\/(ASCE)BE.1943-5592.0001330","article-title":"Accurate deformation monitoring on bridge structures using a cost-effective sensing system combined with a camera and accelerometers: Case study","volume":"24","author":"Xu","year":"2019","journal-title":"J. Bridge Eng."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ymssp.2016.11.021","article-title":"Experimental validation of cost-effective vision-based structural health monitoring","volume":"88","author":"Feng","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1080\/15732479.2016.1164729","article-title":"Computer vision-based displacement and vibration monitoring without using physical target on structures","volume":"13","author":"Khuc","year":"2017","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.ymssp.2018.11.015","article-title":"Development and field testing of a vision-based displacement system using a low cost wireless action camera","volume":"121","author":"Lydon","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"e1977","DOI":"10.1002\/stc.1977","article-title":"Eulerian-based virtual visual sensors to measure dynamic displacements of structures","volume":"24","author":"Shariati","year":"2017","journal-title":"Struct. Control Health Monit."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.measurement.2018.07.055","article-title":"Motion Magnification Analysis for structural monitoring of ancient constructions","volume":"129","author":"Fioriti","year":"2018","journal-title":"Measurement"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"04019062","DOI":"10.1061\/(ASCE)ST.1943-541X.0002321","article-title":"Vision-based modal survey of civil infrastructure using unmanned aerial vehicles","volume":"145","author":"Hoskere","year":"2019","journal-title":"J. Struct. Eng."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"04018207","DOI":"10.1061\/(ASCE)ST.1943-541X.0002203","article-title":"Camera-based vibration measurement of the world war I memorial bridge in Portsmouth, New Hampshire","volume":"144","author":"Chen","year":"2018","journal-title":"J. Struct. Eng."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1111\/mice.12338","article-title":"Structural displacement measurement using an unmanned aerial system","volume":"33","author":"Yoon","year":"2018","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"16557","DOI":"10.3390\/s150716557","article-title":"A vision-based sensor for noncontact structural displacement measurement","volume":"15","author":"Feng","year":"2015","journal-title":"Sensors"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s13349-017-0261-4","article-title":"Review of machine-vision based methodologies for displacement measurement in civil structures","volume":"8","author":"Xu","year":"2018","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"8444","DOI":"10.3390\/s150408444","article-title":"Bridge displacement monitoring method based on laser projection-sensing technology","volume":"15","author":"Zhao","year":"2015","journal-title":"Sensors"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Artese, S., Achilli, V., and Zinno, R. (2018). Monitoring of bridges by a laser pointer: Dynamic measurement of support rotations and elastic line displacements: Methodology and first test. Sensors, 18.","DOI":"10.3390\/s18020338"},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Lee, J., Lee, K.C., Cho, S., and Sim, S.H. (2017). Computer vision-based structural displacement measurement robust to light-induced image degradation for in-service bridges. Sensors, 17.","DOI":"10.3390\/s17102317"},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Vicente, M.A., Gonzalez, D.C., Minguez, J., and Schumacher, T. (2018). A novel laser and video-based displacement transducer to monitor bridge deflections. Sensors, 18.","DOI":"10.3390\/s18040970"},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"04015023","DOI":"10.1061\/(ASCE)BE.1943-5592.0000747","article-title":"Nontarget vision sensor for remote measurement of bridge dynamic response","volume":"20","author":"Feng","year":"2015","journal-title":"J. Bridge Eng."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/0141-0296(93)90054-8","article-title":"Measurements of static and dynamic displacement from visual monitoring of the Humber Bridge","volume":"15","author":"Stephen","year":"1993","journal-title":"Eng. Struct."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.ymssp.2015.06.004","article-title":"Dynamic displacement measurement of large-scale structures based on the Lucas-Kanade template tracking algorithm","volume":"66","author":"Guo","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"315","DOI":"10.12989\/was.2015.20.2.315","article-title":"Multi-point displacement monitoring of bridges using a vision-based approach","volume":"20","author":"Ye","year":"2015","journal-title":"Wind. Struct."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"108951","DOI":"10.1016\/j.ymssp.2022.108951","article-title":"A novel gradient-based matching via voting technique for vision-based structural displacement measurement","volume":"171","author":"Wang","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"109847","DOI":"10.1016\/j.measurement.2021.109847","article-title":"A multi-resolution deep feature framework for dynamic displacement measurement of bridges using vision-based tracking system","volume":"183","author":"Zhu","year":"2021","journal-title":"Measurement"},{"key":"ref_163","first-page":"617","article-title":"A completely non-contact recognition system for bridge unit influence line using portable cameras and computer vision","volume":"24","author":"Dong","year":"2019","journal-title":"Smart Struct. Syst."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.ymssp.2016.11.009","article-title":"The subpixel resolution of optical-flow-based modal analysis","volume":"88","author":"Javh","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ymssp.2017.07.024","article-title":"Measuring full-field displacement spectral components using photographs taken with a DSLR camera via an analogue Fourier integral","volume":"100","author":"Javh","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"109926","DOI":"10.1016\/j.ymssp.2022.109926","article-title":"A new operator based on edge detection for monitoring the cable under different illumination","volume":"187","author":"Xie","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"109931","DOI":"10.1016\/j.ymssp.2022.109931","article-title":"A novel marker for robust and accurate phase-based 2D motion estimation from noisy image data","volume":"187","author":"Miao","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.ymssp.2010.08.013","article-title":"3D digital image correlation methods for full-field vibration measurement","volume":"25","author":"Helfrick","year":"2011","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_169","unstructured":"Warren, C., Niezrecki, C., and Avitabile, P. (2023, January 26\u201329). FRF measurements and mode shapes determined using image-based 3D point-tracking. Proceedings of the 29th IMAC on Structural Dynamics, New York, NY, USA."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s11431-017-9090-x","article-title":"Review of single-camera stereo-digital image correlation techniques for full-field 3D shape and deformation measurement","volume":"61","author":"Pan","year":"2018","journal-title":"Sci. China Technol. Sc."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.ymssp.2019.01.016","article-title":"Low-frame-rate single camera system for 3D full-field high-frequency vibration measurements","volume":"123","author":"Barone","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"113040","DOI":"10.1016\/j.engstruct.2021.113040","article-title":"Computer vision based target-free 3D vibration displacement measurement of structures","volume":"246","author":"Shao","year":"2021","journal-title":"Eng. Struct."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"e3025","DOI":"10.1002\/stc.3025","article-title":"Vision-based displacement measurement using an unmanned aerial vehicle","volume":"29","author":"Han","year":"2022","journal-title":"Struct. Control Health Monit."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1111\/mice.12567","article-title":"Noncontact cable force estimation with unmanned aerial vehicle and computer vision","volume":"36","author":"Tian","year":"2021","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"e2910","DOI":"10.1002\/stc.2910","article-title":"Complex image background segmentation for cable force estimation of urban bridges with drone-captured video and deep learning","volume":"29","author":"Zhang","year":"2022","journal-title":"Struct. Control Health Monit."},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Liu, G., He, C., Zou, C., and Wang, A. (2022). Displacement measurement based on UAV images using SURF-enhanced camera calibration algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14236008"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"4752072","DOI":"10.1155\/2023\/4752072","article-title":"Bridge Deformation Measurement Using Unmanned Aerial Dual Camera and Learning-Based Tracking Method","volume":"2023","author":"Jiang","year":"2023","journal-title":"Struct. Control Health Monit."},{"key":"ref_178","first-page":"110575","article-title":"Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring","volume":"200","author":"Smyth","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"104338","DOI":"10.1016\/j.autcon.2022.104338","article-title":"Structural displacement estimation by fusing vision camera and accelerometer using hybrid computer vision algorithm and adaptive multi-rate Kalman filter","volume":"140","author":"Ma","year":"2022","journal-title":"Automat. Constr."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1111\/mice.12767","article-title":"Real-time structural displacement estimation by fusing asynchronous acceleration and computer vision measurements","volume":"37","author":"Ma","year":"2022","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"e2122","DOI":"10.1002\/stc.2122","article-title":"Visual\u2013inertial displacement sensing using data fusion of vision-based displacement with acceleration","volume":"25","author":"Park","year":"2018","journal-title":"Struct. Control Health Monit."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"114303","DOI":"10.1016\/j.engstruct.2022.114303","article-title":"Accurate structural displacement monitoring by data fusion of a consumer-grade camera and accelerometers","volume":"262","author":"Wu","year":"2022","journal-title":"Eng. Struct."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"112532","DOI":"10.1016\/j.measurement.2023.112532","article-title":"Sparse accelerometer-aided computer vision technology for the accurate full-field displacement estimation of beam-type bridge structures","volume":"212","author":"Wu","year":"2023","journal-title":"Measurement"},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1002\/stc.1819","article-title":"Vision-based multipoint displacement measurement for structural health monitoring","volume":"23","author":"Feng","year":"2016","journal-title":"Struct. Control Health Monit."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"110754","DOI":"10.1016\/j.ymssp.2023.110754","article-title":"Structural displacement estimation by a hybrid computer vision approach","volume":"204","author":"Gao","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_186","doi-asserted-by":"crossref","unstructured":"Chen, Z., Ruan, X., and Zhang, Y. (2023). Vision-Based Dynamic Response Extraction and Modal Identification of Simple Structures Subject to Ambient Excitation. Remote Sens., 15.","DOI":"10.3390\/rs15040962"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.engstruct.2017.11.018","article-title":"Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection\u2014A review","volume":"156","author":"Feng","year":"2018","journal-title":"Eng. Struct."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1002\/stc.1850","article-title":"Target-free approach for vision-based structural system identification using consumer-grade cameras","volume":"23","author":"Yoon","year":"2016","journal-title":"Struct. Control Health Monit."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1061\/(ASCE)0733-9399(2008)134:6(466)","article-title":"Nontarget stereo vision technique for spatiotemporal response measurement of line-like structures","volume":"134","author":"Ji","year":"2008","journal-title":"J. Eng. Mech."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"110551","DOI":"10.1016\/j.engstruct.2020.110551","article-title":"Structural health monitoring and seismic response assessment of bridge structures using target-tracking digital image correlation","volume":"213","author":"Ngeljaratan","year":"2020","journal-title":"Eng. Struct."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"110575","DOI":"10.1016\/j.ymssp.2023.110575","article-title":"Cable vibration measurement based on broad-band phase-based motion magnification and line tracking algorithm","volume":"200","author":"Luo","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2461912.2461966","article-title":"Phase-based video motion processing","volume":"32","author":"Wadhwa","year":"2013","journal-title":"ACM Trans. Graph."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.jsv.2015.01.024","article-title":"Modal identification of simple structures with high-speed video using motion magnification","volume":"345","author":"Chen","year":"2015","journal-title":"J. Sound Vib."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.jsv.2016.11.034","article-title":"Blind identification of full-field vibration modes of output-only structures from uniformly-sampled, possibly temporally-aliased (sub-Nyquist), video measurements","volume":"390","author":"Yang","year":"2017","journal-title":"J. Sound Vib."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.ymssp.2016.08.041","article-title":"Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification","volume":"85","author":"Yang","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.ymssp.2017.09.019","article-title":"High frequency mode shapes characterisation using Digital Image Correlation and phase-based motion magnification","volume":"102","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.ymssp.2018.02.006","article-title":"3D mode shapes characterisation using phase-based motion magnification in large structures using stereoscopic DIC","volume":"108","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"112768","DOI":"10.1016\/j.measurement.2023.112768","article-title":"Structural modal identification using a portable laser-and-camera measurement system","volume":"214","author":"Han","year":"2023","journal-title":"Measurement"},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"109553","DOI":"10.1016\/j.ymssp.2022.109553","article-title":"Time-domain model identification of structural dynamics from spatially dense 3D vision-based measurements","volume":"182","author":"Willems","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.ymssp.2017.05.008","article-title":"High frequency modal identification on noisy high-speed camera data","volume":"98","author":"Javh","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"109927","DOI":"10.1016\/j.ymssp.2022.109927","article-title":"Multi-level curvature-based parametrization and model updating using a 3D full-field response","volume":"187","author":"Zaletelj","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"106287","DOI":"10.1016\/j.ymssp.2019.106287","article-title":"Frequency domain triangulation for full-field 3D operating-deflection-shape identification","volume":"133","author":"Gorjup","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"108232","DOI":"10.1016\/j.ymssp.2021.108232","article-title":"Spatiotemporal compressive sensing of full-field Lagrangian continuous displacement response from optical flow of edge: Identification of full-field dynamic modes","volume":"164","author":"Bhowmick","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_204","doi-asserted-by":"crossref","unstructured":"Kong, X., Yi, J., Wang, X., Luo, K., and Hu, J. (2023). Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking. Appl. Sci., 13.","DOI":"10.3390\/app13020747"},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"e2957","DOI":"10.1002\/stc.2957","article-title":"Modal analysis and tension estimation of stay cables using noncontact vision-based motion magnification method","volume":"29","author":"Wangchuk","year":"2022","journal-title":"Struct. Control Health Monit."},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1177\/1475921719840354","article-title":"Photogrammetry-based structural damage detection by tracking a visible laser line","volume":"19","author":"Xu","year":"2020","journal-title":"Struct. Health Monit."},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.engstruct.2016.11.038","article-title":"Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters","volume":"132","author":"Cha","year":"2017","journal-title":"Eng. Struct."},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"04017202","DOI":"10.1061\/(ASCE)ST.1943-541X.0001925","article-title":"Structural identification using computer vision\u2013based bridge health monitoring","volume":"144","author":"Khuc","year":"2018","journal-title":"J. Struct. Eng."},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"109320","DOI":"10.1016\/j.ymssp.2022.109320","article-title":"Vibration-based structural damage detection via phase-based motion estimation using convolutional neural networks","volume":"178","author":"Zhang","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"110327","DOI":"10.1016\/j.ymssp.2023.110327","article-title":"Model-informed deep learning strategy with vision measurement for damage identification of truss structures","volume":"196","author":"Shu","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"109631","DOI":"10.1016\/j.ymssp.2022.109631","article-title":"A hybrid method for damage detection and condition assessment of hinge joints in hollow slab bridges using physical models and vision-based measurements","volume":"183","author":"Hu","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1111\/mice.12434","article-title":"A methodology for obtaining spatiotemporal information of the vehicles on bridges based on computer vision","volume":"34","author":"Zhang","year":"2019","journal-title":"Comput. Aided Civil Infrastruct. Eng."},{"key":"ref_213","first-page":"1559","article-title":"Motion detection based on frame difference method","volume":"4","author":"Singla","year":"2014","journal-title":"Int. J. Inform. Comput. Technol."},{"key":"ref_214","doi-asserted-by":"crossref","unstructured":"Bai, Z., Gao, Q., and Yu, X. (2019, January 4\u20137). Moving object detection based on adaptive loci frame difference method. Proceedings of the 2019 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China. Available online: https:\/\/ieeexplore.ieee.org\/document\/8816624.","DOI":"10.1109\/ICMA.2019.8816624"},{"key":"ref_215","doi-asserted-by":"crossref","unstructured":"Liang, R., Yan, L., Gao, P., Qian, X., Zhang, Z., and Sun, H. (2010, January 16\u201318). Aviation video moving-target detection with inter-frame difference. Proceedings of the 2010 3rd International Congress on Image and Signal Processing, Yantai, China. Available online: https:\/\/ieeexplore.ieee.org\/document\/5646303.","DOI":"10.1109\/CISP.2010.5646303"},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"2705","DOI":"10.1016\/j.proeng.2012.01.376","article-title":"Three-frame difference algorithm research based on mathematical morphology","volume":"29","author":"Zhang","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_217","doi-asserted-by":"crossref","unstructured":"Zuo, F., and Gao, S. (2009, January 18\u201320). Moving Object Detection and Tracking Based on WADM. Proceedings of the 2009 International Symposium on Computer Network and Multimedia Technology, Wuhan, China. Available online: https:\/\/ieeexplore.ieee.org\/document\/5374584.","DOI":"10.1109\/CNMT.2009.5374584"},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/TIP.2010.2101613","article-title":"ViBe: A universal background subtraction algorithm for video sequences","volume":"20","author":"Barnich","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_219","doi-asserted-by":"crossref","unstructured":"Cioppa, A., Braham, M., and Van Droogenbroeck, M. (2020). Asynchronous semantic background subtraction. J. Imaging, 6.","DOI":"10.3390\/jimaging6060050"},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","article-title":"Determining optical flow","volume":"17","author":"Horn","year":"1981","journal-title":"Artif. Intell."},{"key":"ref_221","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, Y.C., and Berg, A.C. (2016, January 14\u201316). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_222","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_223","first-page":"2980","article-title":"Mask r-cnn","volume":"42","author":"He","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_225","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., and Brox, T. (2017, January 21\u201326). Flownet 2.0: Evolution of optical flow estimation with deep networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, HI, USA.","DOI":"10.1109\/CVPR.2017.179"},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1631\/FITEE.1900336","article-title":"Multi-focus image fusion based on fully convolutional networks","volume":"21","author":"Guo","year":"2020","journal-title":"Front. Inform. Technol. Electron. Eng."},{"key":"ref_227","doi-asserted-by":"crossref","unstructured":"Branson, S.J., Van Horn, G., Belongie, S., and Perona, P. (2014). Bird species categorization using pose normalized deep convolutional nets. arXiv.","DOI":"10.5244\/C.28.87"},{"key":"ref_228","doi-asserted-by":"crossref","unstructured":"Zhang, N., Donahue, J., Girshick, R., and Donahue, J. (2014, January 6\u201312). Part-based R-CNNs for fine-grained category detection. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_54"},{"key":"ref_229","unstructured":"Xiao, T., Xu, Y., Yang, K., Peng, Y., and Zhang, Z. (2014, January 23\u201328). The application of two-level attention models in deep convolutional neural network for fine-grained image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA."},{"key":"ref_230","doi-asserted-by":"crossref","unstructured":"Simon, M., Rodner, E., and Denzler, J. (2014, January 1\u20135). Part detector discovery in deep convolutional neural networks. Proceedings of the Computer Vision\u2014ACCV 2014: 12th Asian Conference on Computer Vision, Singapore.","DOI":"10.1007\/978-3-319-16808-1_12"},{"key":"ref_231","doi-asserted-by":"crossref","unstructured":"Simon, M., and Rodner, E. (2015, January 7\u201313). Neural activation constellations: Unsupervised part model discovery with convolutional networks. Proceedings of the Computer Vision--ACCV 2014: 12th Asian Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.136"},{"key":"ref_232","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1016\/j.jvcir.2016.08.011","article-title":"Vehicle Tracking and Speed Measurement system (VTSM) based on novel feature descriptor: Diagonal Hexadecimal Pattern (DHP)","volume":"40","author":"Jeyabharathi","year":"2016","journal-title":"J. Visual Commun. Image Represent."},{"key":"ref_233","doi-asserted-by":"crossref","first-page":"4805","DOI":"10.3390\/s100504805","article-title":"Real time speed estimation of moving vehicles from side view images from an uncalibrated video camera","volume":"10","author":"Temiz","year":"2010","journal-title":"Sensors"},{"key":"ref_234","doi-asserted-by":"crossref","unstructured":"Hua, S., Kapoor, M., and Anastasiu, D.C. (2018, January 18\u201322). Vehicle tracking and speed estimation from traffic videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00028"},{"key":"ref_235","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.ijleo.2013.06.036","article-title":"Vehicle speed measurement based on gray constraint optical flow algorithm","volume":"125","author":"Lan","year":"2014","journal-title":"Optik"},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.compeleceng.2019.04.001","article-title":"Vehicle speed measurement model for video-based systems","volume":"76","author":"Javadi","year":"2019","journal-title":"Comput. Electr. Eng."},{"key":"ref_237","doi-asserted-by":"crossref","unstructured":"Dahl, M., and Javadi, S. (2019). Analytical modeling for a video-based vehicle speed measurement framework. Sensors, 20.","DOI":"10.3390\/s20010160"},{"key":"ref_238","unstructured":"Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., Andriluka, M., Rajpurkar, P., Migimatsu, T., and Cheng-Yue, R. (2015). An empirical evaluation of deep learning on highway driving. arXiv."},{"key":"ref_239","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1109\/TNNLS.2016.2522428","article-title":"Deep neural network for structural prediction and lane detection in traffic scene","volume":"28","author":"Li","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_240","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1007\/978-3-319-12637-1_57","article-title":"Robust Lane detection based on convolutional neural network and random sample consensus","volume":"8834","author":"Kim","year":"2014","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_241","unstructured":"Lee, S., Kim, J., Shin Yoon, J., Shin, S., Bailo, O., Kim, N., Lee, T., Hong, S.H., Han, S., and So Kweon, I. (2014, January 3\u20136). VPGNet: Vanishing point guided network for lane and road marking detection and recognition. Proceedings of the Neural Information Processing: 21st International Conference, ICONIP 2014, Kuching, Malaysia."},{"key":"ref_242","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1002\/stc.1780","article-title":"Identification of spatio-temporal distribution of vehicle loads on long-span bridges using computer vision technology","volume":"23","author":"Chen","year":"2016","journal-title":"Struct. Control Health Monit."},{"key":"ref_243","first-page":"83","article-title":"Vehicle tracking for bridge load dynamics using vision techniques","volume":"7","author":"Brown","year":"2016","journal-title":"Struct. Health Monit."},{"key":"ref_244","doi-asserted-by":"crossref","first-page":"04016032","DOI":"10.1061\/(ASCE)BE.1943-5592.0000776","article-title":"Contactless bridge weigh-in-motion","volume":"21","author":"Ojio","year":"2016","journal-title":"J. Bridge Eng."},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1007\/s10043-012-0063-1","article-title":"Vision-based vehicle detection and inter-vehicle distance estimation for driver alarm system","volume":"19","author":"Kim","year":"2012","journal-title":"Opt. Rev."},{"key":"ref_246","doi-asserted-by":"crossref","first-page":"923632","DOI":"10.1155\/2014\/923632","article-title":"Robust range estimation with a monocular camera for vision-based forward collision warning system","volume":"2014","author":"Park","year":"2014","journal-title":"Sci. World J."},{"key":"ref_247","doi-asserted-by":"crossref","first-page":"012043","DOI":"10.1088\/1757-899X\/533\/1\/012043","article-title":"Research on the forward distance detection algorithm based on the camera switching","volume":"533","author":"Chen","year":"2019","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"363","DOI":"10.12989\/sss.2013.12.3_4.363","article-title":"A vision-based system for dynamic displacement measurement of long-span bridges: Algorithm and verification","volume":"12","author":"Ye","year":"2013","journal-title":"Smart Struct. Syst."},{"key":"ref_249","doi-asserted-by":"crossref","first-page":"04016141","DOI":"10.1061\/(ASCE)BE.1943-5592.0001019","article-title":"Novel virtual simply supported beam method for detecting the speed and axles of moving vehicles on bridges","volume":"22","author":"He","year":"2017","journal-title":"J. Bridge Eng."},{"key":"ref_250","doi-asserted-by":"crossref","first-page":"04019086","DOI":"10.1061\/(ASCE)BE.1943-5592.0001474","article-title":"Virtual axle method for bridge weigh-in-motion systems requiring no axle detector","volume":"24","author":"He","year":"2019","journal-title":"J. Bridge Eng."},{"key":"ref_251","doi-asserted-by":"crossref","unstructured":"Ding, Y., Zhou, D., Wang, Z., Li, M., and Yu, S. (2019, January 6\u20138). Overload and load centroid recognition method based on vertical displacement of body. Proceedings of the 2019 34th Youth Academic Annual Conference of Chinese Association of Automation (YAC), Jinzhou, China.","DOI":"10.1109\/YAC.2019.8787717"},{"key":"ref_252","doi-asserted-by":"crossref","first-page":"102711","DOI":"10.1016\/j.advengsoft.2019.102711","article-title":"Evaluation of the extreme traffic load effects on the Forth Road Bridge using image analysis of traffic data","volume":"137","author":"Micu","year":"2019","journal-title":"Adv. Eng. Softw."},{"key":"ref_253","doi-asserted-by":"crossref","first-page":"107801","DOI":"10.1016\/j.measurement.2020.107801","article-title":"Vehicle weight identification system for spatiotemporal load distribution on bridges based on non-contact machine vision technology and deep learning algorithms","volume":"159","author":"Zhou","year":"2020","journal-title":"Measurement"},{"key":"ref_254","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.measurement.2019.05.042","article-title":"Identification of moving loads based on the information fusion of weigh-in-motion system and multiple camera machine vision","volume":"144","author":"Dan","year":"2019","journal-title":"Measurement"},{"key":"ref_255","doi-asserted-by":"crossref","first-page":"107415","DOI":"10.1016\/j.measurement.2019.107415","article-title":"Non-contact vehicle weigh-in-motion using computer vision","volume":"153","author":"Feng","year":"2020","journal-title":"Measurement"},{"key":"ref_256","doi-asserted-by":"crossref","first-page":"11588","DOI":"10.1109\/JSEN.2020.3038186","article-title":"Application of computer vision for estimation of moving vehicle weight","volume":"21","author":"Feng","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_257","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1177\/1475921710373290","article-title":"Structural health monitoring using video stream, influence lines, and statistical analysis","volume":"10","author":"Zaurin","year":"2011","journal-title":"Struct. Health Monit."},{"key":"ref_258","doi-asserted-by":"crossref","first-page":"015019","DOI":"10.1088\/0964-1726\/19\/1\/015019","article-title":"Integration of computer imaging and sensor data for structural health monitoring of bridges","volume":"19","author":"Zaurin","year":"2009","journal-title":"Smart Mater. Struct."},{"key":"ref_259","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1061\/(ASCE)BE.1943-5592.0000288","article-title":"Sensor networks, computer imaging, and unit influence lines for structural health monitoring: Case study for bridge load rating","volume":"17","author":"Catbas","year":"2012","journal-title":"J. Bridge Eng."},{"key":"ref_260","doi-asserted-by":"crossref","first-page":"05016002","DOI":"10.1061\/(ASCE)BE.1943-5592.0000811","article-title":"Hybrid sensor-camera monitoring for damage detection: Case study of a real bridge","volume":"21","author":"Zaurin","year":"2016","journal-title":"J. Bridge Eng."},{"key":"ref_261","doi-asserted-by":"crossref","first-page":"3409525","DOI":"10.1155\/2019\/3409525","article-title":"Traffic sensing methodology combining influence line theory and computer vision techniques for girder bridges","volume":"2019","author":"Jian","year":"2019","journal-title":"J. Sens."},{"key":"ref_262","doi-asserted-by":"crossref","first-page":"e2271","DOI":"10.1002\/stc.2271","article-title":"A novel computer vision-based monitoring methodology for vehicle-induced aerodynamic load on noise barrier","volume":"25","author":"Pan","year":"2018","journal-title":"Struct. Control Health Monit."},{"key":"ref_263","doi-asserted-by":"crossref","unstructured":"Xia, Y., Jian, X., Yan, B., and Su, D. (2019). Infrastructure safety oriented traffic load monitoring using multi-sensor and single camera for short and medium span bridges. Remote Sens., 11.","DOI":"10.3390\/rs11222651"},{"key":"ref_264","doi-asserted-by":"crossref","first-page":"e2636","DOI":"10.1002\/stc.2636","article-title":"An accurate and robust monitoring method of full-bridge traffic load distribution based on YOLO-v3 machine vision","volume":"27","author":"Ge","year":"2020","journal-title":"Struct. Control Health Monit."},{"key":"ref_265","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1007\/s00034-019-01224-9","article-title":"An efficient optimal neural network-based moving vehicle detection in traffic video surveillance system","volume":"39","author":"Appathurai","year":"2020","journal-title":"Circuits Syst. Signal Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7863\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:50:23Z","timestamp":1760129423000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7863"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,13]]},"references-count":265,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23187863"],"URL":"https:\/\/doi.org\/10.3390\/s23187863","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,13]]}}}