{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:49:30Z","timestamp":1780512570008,"version":"3.54.1"},"reference-count":115,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T00:00:00Z","timestamp":1628812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cyber\u2013physical systems (CPSs) are generally considered to be the next generation of engineered systems. However, the actual application of CPSs in the Architecture, Engineering and Construction (AEC) industry is still at a low level. The sensing method in the construction process plays a very important role in the establishment of CPSs. Therefore, the purpose of this paper is to discuss the application potential of computer vision-based sensing methods and provide practical suggestions through a literature review. This paper provides a review of the current application of CPSs in the AEC industry, summarizes the current knowledge gaps, and discusses the problems with the current construction site sensing approach. Considering the unique advantages of the computer vision (CV) method at the construction site, the application of CV for different construction entities was reviewed and summarized to achieve a CV-based construction site sensing approach for construction process CPSs. The potential of CPS can be further stimulated by providing rich information from on-site sensing using CV methods. According to the review, this approach has unique advantages in the specific environment of the construction site. Based on the current knowledge gap identified in the literature review, this paper proposes a novel concept of visual-based construction site sensing method for CPS application, and an architecture for CV-based CPS is proposed as an implementation of this concept. The main contribution of this paper is to propose a CPS architecture using computer vision as the main information acquisition method based on the literature review. This architecture innovatively introduces computer vision as a sensing method of construction sites, and realizes low-cost and non-invasive information acquisition in complex construction scenarios. This method can be used as an important supplement to on-site sensing to further promote the automation and intelligence of the construction process.<\/jats:p>","DOI":"10.3390\/s21165468","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T09:22:38Z","timestamp":1628846558000},"page":"5468","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Computer Vision-Based Construction Process Sensing for Cyber\u2013Physical Systems: A Review"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3353-7715","authenticated-orcid":false,"given":"Binghan","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Structural Engineering, Tongji University, 1239 Siping, Shanghai 200092, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7175-8001","authenticated-orcid":false,"given":"Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Structural Engineering, Tongji University, 1239 Siping, Shanghai 200092, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congjun","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhongyifeng Construction Group Co., Suzhou 215131, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhichen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Structural Engineering, Tongji University, 1239 Siping, Shanghai 200092, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boda","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Structural Engineering, Tongji University, 1239 Siping, Shanghai 200092, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tengwei","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Structural Engineering, Tongji University, 1239 Siping, Shanghai 200092, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/JPROC.2011.2160929","article-title":"Modeling Cyber\u2013Physical Systems","volume":"100","author":"Derler","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lee, E.A. (2008). Cyber Physical Systems: Design Challenges, IEEE.","DOI":"10.1109\/ISORC.2008.25"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Madubuike, O.C., and Anumba, C.J. (2020). Potential for the Integration of Cyber-Physical Systems with Intelligent Buildings, Construction Research Congress 2020: Computer Applications, 2020, American Society of Civil Engineers.","DOI":"10.1061\/9780784482865.074"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Rajkumar, R., Lee, I., Sha, L., and Stankovic, J. (2010). Cyber-Physical Systems: The Next Computing Revolution, IEEE.","DOI":"10.1145\/1837274.1837461"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.mfglet.2014.12.001","article-title":"A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems","volume":"3","author":"Lee","year":"2015","journal-title":"Manuf. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/JAS.2017.7510349","article-title":"Review on cyber-physical systems","volume":"4","author":"Liu","year":"2017","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_7","first-page":"161","article-title":"Cyber physical Systems","volume":"12","author":"Baheti","year":"2011","journal-title":"Impact Control Technol."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_10","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1108\/ECAM-04-2017-0066","article-title":"Digital skin of the construction site","volume":"26","author":"Edirisinghe","year":"2019","journal-title":"Eng. Constr. Arch. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tang, L.-A., Yu, X., Kim, S., Han, J., Hung, C.-C., and Peng, W.-C. (2010, January 13\u201317). Tru-Alarm: Trustworthiness Analysis of Sensor Networks in Cyber-Physical Systems. Proceedings of the 2010 IEEE International Conference on Data Mining, Sydney, Australia.","DOI":"10.1109\/ICDM.2010.63"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.comnet.2018.07.017","article-title":"Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues","volume":"144","year":"2018","journal-title":"Comput. Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.autcon.2017.03.005","article-title":"Bibliometric analysis and review of Building Information Modelling literature pub-lished between 2005 and 2015","volume":"80","author":"Santos","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.aei.2019.01.005","article-title":"BIM-enabled facilities operation and maintenance: A review","volume":"39","author":"Gao","year":"2019","journal-title":"Adv. Eng. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/j.cirp.2016.06.005","article-title":"Cyber-physical systems in manufacturing","volume":"65","author":"Monostori","year":"2016","journal-title":"CIRP Ann."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4837","DOI":"10.3390\/s150304837","article-title":"The Past, Present and Future of Cyber-Physical Systems: A Focus on Models","volume":"15","author":"Lee","year":"2015","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1109\/JPROC.2012.2189792","article-title":"Cyber\u2013Physical Systems: A Perspective at the Centennial","volume":"100","author":"Kim","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Griffor, E.R., Greer, C., Wollman, D., and Burns, M.J. (2017). Framework for Cyber-Physical Systems: Volume 1, Overview, NIST Special Publication.","DOI":"10.6028\/NIST.SP.1500-201"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102837","DOI":"10.1016\/j.autcon.2019.102837","article-title":"Digital twinning of existing reinforced concrete bridges from labelled point clusters","volume":"105","author":"Lu","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.jmsy.2021.05.011","article-title":"Digital twins-based smart manufacturing system design in Indus-try 4.0: A review","volume":"60","author":"Leng","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Leng, J., Zhou, M., Xiao, Y., Zhang, H., Liu, Q., Shen, W., Su, Q., and Li, L. (2021). Digital twins-based remote semi-physical com-missioning of flow-type smart manufacturing systems. J. Clean Prod., 306.","DOI":"10.1016\/j.jclepro.2021.127278"},{"key":"ref_24","unstructured":"Leng, J., Yan, D., Liu, Q., Zhang, H., Zhao, G., Wei, L., Zhang, D., Yu, A., and Chen, X. (2019). Digital twin-driven joint optimisation of packing and storage assignment in large-scale automated high-rise warehouse product-service system. Int. J. Comput. Integr. Manuf., 1\u201318."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101895","DOI":"10.1016\/j.rcim.2019.101895","article-title":"Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model","volume":"63","author":"Leng","year":"2020","journal-title":"Robot. Comput. Manuf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1108\/ECAM-07-2014-0097","article-title":"Cyber-physical systems integration of building information models and the physical con-struction","volume":"22","author":"Akanmu","year":"2015","journal-title":"Eng. Constr. Archit. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.autcon.2012.10.017","article-title":"Real-time resource location data collection and visualization technology for construction safety and activity monitoring applications","volume":"34","author":"Cheng","year":"2013","journal-title":"Autom. Constr."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.autcon.2019.02.010","article-title":"A cyber-physical system approach for building efficiency monitoring","volume":"102","author":"Bonci","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, Y.-Y., Kang, K., Lin, J.-R., Zhang, J.-P., and Zhang, Y. (2020). Building information modeling\u2013based cyber-physical platform for building performance monitoring. Int. J. Distrib. Sens. Netw., 16.","DOI":"10.1177\/1550147720908170"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fitz, T., Theiler, M., and Smarsly, K. (2019). A metamodel for cyber-physical systems. Adv. Eng. Inform., 41.","DOI":"10.1016\/j.aei.2019.100930"},{"key":"ref_31","first-page":"240","article-title":"A cyber\u2013physical system (CPS) for planning and monitoring mobile cranes on construction sites","volume":"171","author":"Kan","year":"2018","journal-title":"Proc. Inst. Civ. Eng. Manag. Procure. Law"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Akanmu, A.A., Olayiwola, J., Ogunseiju, O., and McFeeters, D. (2020). Cyber-physical postural training system for construction workers. Autom. Constr., 117.","DOI":"10.1016\/j.autcon.2020.103272"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.autcon.2018.10.017","article-title":"Cyber-physical-system-based safety monitoring for blind hoisting with the internet of things: A case study","volume":"97","author":"Zhou","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bavaresco, M.V., D\u2019Oca, S., Ghisi, E., and Lamberts, R. (2019). Technological innovations to assess and include the human dimen-sion in the building-performance loop: A review. Energ Build., 202.","DOI":"10.1016\/j.enbuild.2019.109365"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s42524-019-0037-0","article-title":"Development of a BIM-based holonic system for re-al-time monitoring of building operational efficiency","volume":"7","author":"Carbonari","year":"2020","journal-title":"Front. Eng. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1111\/mice.12431","article-title":"Cyber-physical approach to the optimization of semiactive structural control under multiple earthquake ground motions","volume":"34","author":"Zhang","year":"2019","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.autcon.2016.02.005","article-title":"Cyber-physical systems for temporary structure monitoring","volume":"66","author":"Yuan","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Maskuriy, R., Selamat, A., Ali, K.N., Maresova, P., and Krejcar, O. (2019). Industry 4.0 for the Construction Industry\u2014How Ready Is the Industry?. Appl. Sci., 9.","DOI":"10.3390\/app9142819"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Maskuriy, R., Selamat, A., Maresova, P., and Krejcar, O. (2019). Olalekan Industry 4.0 for the Construction Industry: Review of Management Perspective. Economies, 7.","DOI":"10.3390\/economies7030068"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Linares, D.A., Anumba, C., and Roofigari-Esfahan, N. (2019). Overview of Supporting Technologies for Cyber-Physical Systems Implementation in the AEC Industry, American Society of Civil Engineers.","DOI":"10.1061\/9780784482438.063"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1007\/s11831-020-09457-7","article-title":"What is at the Root of Construction 4.0: A Systematic Review of the Recent Research Effort","volume":"28","author":"Boton","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1007\/s00607-016-0509-6","article-title":"Architecting dynamic cyber-physical spaces","volume":"98","author":"Tsigkanos","year":"2016","journal-title":"Computing"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/JIOT.2013.2296516","article-title":"An Information Framework for Creating a Smart City through Internet of Things","volume":"1","author":"Jin","year":"2014","journal-title":"IEEE Internet Things J."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Habibzadeh, H., Nussbaum, B.H., Anjomshoa, F., Kantarci, B., and Soyata, T. (2019). A survey on cybersecurity, data privacy, and policy issues in cyber-physical system deployments in smart cities. Sustain. Cities Soc., 50.","DOI":"10.1016\/j.scs.2019.101660"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1108\/ECAM-10-2019-0578","article-title":"Cyber physical system for safety management in smart construction site","volume":"28","author":"Jiang","year":"2020","journal-title":"Eng. Constr. Arch. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kan, C., Anumba, C.J., and Messner, J.I. (2020). A Cyber-Physical Systems Approach for Improved Mobile Crane Safety: Site Implementation, American Society of Civil Engineers.","DOI":"10.1061\/9780784482865.110"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/s11831-020-09455-9","article-title":"Methodological-Technological Framework for Construction 4","volume":"28","author":"Rivera","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_48","first-page":"27","article-title":"State-of-the-art of intelligent building envelopes in the context of intelligent technical systems","volume":"11","author":"Knaack","year":"2018","journal-title":"Intell. Build. Int."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object Detection with Deep Learning: A Review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_50","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_51","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_52","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","article-title":"Mask R-CNN","volume":"42","author":"He","year":"2020","journal-title":"IEEE T Pattern Anal."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","article-title":"SSD: Single Shot MultiBox Detector","volume":"Volume 9905","author":"Leibe","year":"2016","journal-title":"Lecture Notes in Computer Science"},{"key":"ref_56","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4 Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_59","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, X., Girshick, R., He, K., and Dollar, P. (2019, January 27\u201328). TensorMask: A Foundation for Dense Object Segmentation. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00215"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Wu, Y., He, K., and Girshick, R. (2020, January 14\u201319). PointRend: Image Segmentation as Rendering. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00982"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Wang, Z., Yang, B., Lei, K., Zhang, B., and Liu, B. (2021). Reidentification-Based Automated Matching for 3D Localization of Workers in Construction Sites. J. Comput. Civil Eng., 35.","DOI":"10.1061\/(ASCE)CP.1943-5487.0000975"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.autcon.2019.01.018","article-title":"Adaptive computer vision-based 2D tracking of workers in complex environments","volume":"103","author":"Konstantinou","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.autcon.2012.06.001","article-title":"Construction worker detection in video frames for initializing vision trackers","volume":"28","author":"Park","year":"2012","journal-title":"Autom. Constr."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.autcon.2012.12.002","article-title":"Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors","volume":"32","author":"Memarzadeh","year":"2013","journal-title":"Autom. Constr."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.autcon.2016.08.039","article-title":"Continuous localization of construction workers via integration of detection and tracking","volume":"72","author":"Park","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.autcon.2017.09.022","article-title":"Identifying poses of safe and productive masons using machine learning","volume":"84","author":"Alwasel","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.autcon.2017.05.005","article-title":"Integrated detection and tracking of workforce and equipment from construction jobsite videos","volume":"81","author":"Zhu","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.autcon.2017.11.002","article-title":"A deep hybrid learning model to detect unsafe behav-ior: Integrating convolution neural networks and long short-term memory","volume":"86","author":"Ding","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Konstantinou, E., and Brilakis, I. (2018). Matching Construction Workers across Views for Automated 3D Vision Tracking On-Site. J. Constr. Eng. Manag., 144.","DOI":"10.1061\/(ASCE)CO.1943-7862.0001508"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.autcon.2018.06.007","article-title":"Convolutional neural networks: Computer vi-sion-based workforce activity assessment in construction","volume":"94","author":"Luo","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.autcon.2018.05.033","article-title":"Ergonomic posture recognition using 3D view-invariant features from single ordinary camera","volume":"94","author":"Zhang","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.autcon.2018.11.017","article-title":"3D tracking of multiple onsite workers based on stereo vision","volume":"98","author":"Lee","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.autcon.2018.11.033","article-title":"Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks","volume":"99","author":"Son","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.autcon.2019.02.020","article-title":"An automatic and non-invasive physical fatigue assessment method for construction workers","volume":"103","author":"Yu","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Yu, B., Niu, Z., Wang, L., and Liu, Y. (2012, January 7\u20138). An automatic and effective approach in identifying tower cranes. Proceedings of the Fourth International Conference on Digital Image Processing (ICDIP 2012), Kuala Lumpur, Malaysia.","DOI":"10.1117\/12.946016"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Li, Y., Gong, L., Song, J., Huang, Y., and Liu, C. (2013, January 4\u20137). ARM based load and hook measuring and tracking for precision hoist of tower crane. Proceedings of the 2013 IEEE International Conference on Mechatronics and Automation, Kagawa, Japan.","DOI":"10.1109\/ICMA.2013.6618083"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1061\/(ASCE)CP.1943-5487.0000242","article-title":"Vision-Based Tower Crane Tracking for Understanding Construction Activity","volume":"28","author":"Yang","year":"2014","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.autcon.2017.06.023","article-title":"Skeleton estimation of excavator by detecting its parts","volume":"82","author":"Soltani","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.aei.2018.06.002","article-title":"Real-time validation of vision-based over-height vehicle detection system","volume":"38","author":"Nguyen","year":"2018","journal-title":"Adv. Eng. Inform."},{"key":"ref_82","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":"2018","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.autcon.2019.03.025","article-title":"Action recognition of earthmoving excavators based on sequential pattern analysis of visual features and operation cycles","volume":"104","author":"Kim","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.autcon.2019.04.004","article-title":"A vision-based marker-less pose estimation system for articulated construction robots","volume":"104","author":"Liang","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yuan, Y., Zhang, M., Zhao, X., Zhang, Y., and Tian, B. (2019). Safety Distance Identification for Crane Drivers Based on Mask R-CNN. Sensors, 19.","DOI":"10.3390\/s19122789"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Guo, Y., Xu, Y., and Li, S. (2020). Dense construction vehicle detection based on orientation-aware feature fusion convolutional neural network. Autom. Constr., 112.","DOI":"10.1016\/j.autcon.2020.103124"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Luo, H., Wang, M., Wong, P.K., and Cheng, J.C.P. (2020). Full body pose estimation of construction equipment using computer vi-sion and deep learning techniques. Autom. Constr., 110.","DOI":"10.1016\/j.autcon.2019.103016"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Kim, J., and Chi, S. (2020). Multi-camera vision-based productivity monitoring of earthmoving operations. Autom. Constr., 112.","DOI":"10.1016\/j.autcon.2020.103121"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1111\/mice.12536","article-title":"Computer vision-based recognition of 3D relationship between construction entities for monitoring struck-by accidents","volume":"35","author":"Yan","year":"2020","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, Q., Yang, B., Wu, T., Lei, K., Zhang, B., and Fang, T. (2021). Vision-Based Framework for Automatic Progress Monitoring of Precast Walls by Using Surveillance Videos during the Construction Phase. J. Comput. Civ. Eng., 35.","DOI":"10.1061\/(ASCE)CP.1943-5487.0000933"},{"key":"ref_91","unstructured":"Zhang, X., Ma, M., He, T., and Xu, X. (2017, January 6\u20139). Steel Bars Counting Method Based on Image and Video Processing. Proceedings of the 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Xiamen, China."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, J., Ma, M., Chen, Z., Yue, S., He, T., and Xu, X. (2018). A High Precision Quality Inspection System for Steel Bars Based on Machine Vision. Sensors, 18.","DOI":"10.3390\/s18082732"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.autcon.2019.01.022","article-title":"Computer vision for real-time extrusion quality monitoring and control in robotic construction","volume":"101","author":"Kazemian","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Deng, H., Hong, H., Luo, D., Deng, Y., and Su, C. (2020). Automatic Indoor Construction Process Monitoring for Tiles Based on BIM and Computer Vision. J. Constr. Eng. Manag., 146.","DOI":"10.1061\/(ASCE)CO.1943-7862.0001744"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Roberts, D., and Golparvar-Fard, M. (2019). End-to-end vision-based detection, tracking and activity analysis of earthmoving equipment filmed at ground level. Autom. Constr., 105.","DOI":"10.1016\/j.autcon.2019.04.006"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1016\/j.autcon.2011.03.005","article-title":"Progressive 3D reconstruction of infrastructure with videogrammetry","volume":"20","author":"Brilakis","year":"2011","journal-title":"Autom. Constr."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1061\/(ASCE)CP.1943-5487.0000168","article-title":"Three-Dimensional Tracking of Construction Resources Using an On-Site Camera System","volume":"26","author":"Park","year":"2012","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.autcon.2015.12.022","article-title":"3D terrain reconstruction of construction sites using a stereo camera","volume":"64","author":"Sung","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.autcon.2017.10.027","article-title":"Interior construction state recognition with 4D BIM registered image sequences","volume":"86","author":"Kropp","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.autcon.2018.02.016","article-title":"Image-based semantic construction reconstruction","volume":"90","author":"Liu","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.autcon.2018.03.011","article-title":"Automatic matching of construction onsite resources under camera views","volume":"91","author":"Zhang","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.autcon.2016.09.002","article-title":"Rahbin: A quadcopter unmanned aerial vehicle based on a systematic image processing approach toward an automated asphalt pavement inspection","volume":"72","author":"Zakeri","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_103","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. Inf."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1111\/mice.12367","article-title":"A Fast Detection Method via Region-Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects","volume":"33","author":"Xue","year":"2018","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"39442","DOI":"10.1109\/ACCESS.2018.2855144","article-title":"Application of Internet of Things Technology and Convolutional Neural Network Model in Bridge Crack Detection","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.autcon.2019.03.003","article-title":"Automatic damage detection of historic masonry buildings based on mobile deep learning","volume":"103","author":"Wang","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.autcon.2017.05.002","article-title":"An experimental study of real-time identification of construction workers\u2019 unsafe behaviors","volume":"82","author":"Yu","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.autcon.2017.09.018","article-title":"Detecting non-hardhat-use by a deep learning method from farfield surveillance videos","volume":"85","author":"Fang","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.autcon.2018.02.018","article-title":"Falls from heights: A computer vision-based approach for safety harness detection","volume":"91","author":"Fang","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.autcon.2018.01.003","article-title":"Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images","volume":"89","author":"Kolar","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Wu, J., Cai, N., Chen, W., Wang, H., and Wang, G. (2019). Automatic detection of hardhats worn by construction personnel: A deep learning approach and benchmark dataset. Autom. Constr., 106.","DOI":"10.1016\/j.autcon.2019.102894"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1108\/ECAM-12-2019-0732","article-title":"Dynamic safety prewarning mechanism of human\u2013machine\u2013environment using computer vision","volume":"27","author":"Xu","year":"2020","journal-title":"Eng. Constr. Archit. Manag."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1108\/ECAM-06-2019-0325","article-title":"Proactive warning system for the crossroads at construction sites based on computer vision","volume":"27","author":"Zhu","year":"2020","journal-title":"Eng. Constr. Archit. Manag."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1108\/ECAM-12-2018-0556","article-title":"BrIM and UAS for bridge inspections and management","volume":"27","author":"Xu","year":"2019","journal-title":"Eng. Constr. Arch. Manag."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1016\/j.autcon.2011.03.007","article-title":"Comparative study of vision tracking methods for tracking of construction site resources","volume":"20","author":"Park","year":"2011","journal-title":"Autom. 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