{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:03:52Z","timestamp":1772903032772,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T00:00:00Z","timestamp":1611619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of Shaanxi","award":["2020ZDLGY09\u201003"],"award-info":[{"award-number":["2020ZDLGY09\u201003"]}]},{"DOI":"10.13039\/501100017691","name":"Key Research and Development Program of Guangxi","doi-asserted-by":"publisher","award":["AB20159032"],"award-info":[{"award-number":["AB20159032"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and 402 Technology Bureau of Xi\u2019an project","award":["2020KJRC0130"],"award-info":[{"award-number":["2020KJRC0130"]}]},{"name":"Open Fund of the Inner Mongolia Transportation 403 Development Research Center","award":["2019KFJJ\u2010006"],"award-info":[{"award-number":["2019KFJJ\u2010006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cracks and exposed steel bars are the main factors that affect the service life of bridges. It is necessary to detect the surface damage during regular bridge inspections. Due to the complex structure of bridges, automatically detecting bridge damage is a challenging task. In the field of crack classification and segmentation, convolutional neural networks have offer advantages, but ordinary networks cannot completely solve the environmental impact problems in reality. To further overcome these problems, in this paper a new algorithm to detect surface damage called EMA-DenseNet is proposed. The main contribution of this article is to redesign the structure of the densely connected convolutional networks (DenseNet) and add the expected maximum attention (EMA) module after the last pooling layer. The EMA module is obviously helpful to the bridge damage feature extraction. Besides, we use a new loss function which considers the connectivity of pixels, it has been proved to be effective in reducing the break point of fracture prediction and improving the accuracy. To train and test the model, we captured many images from multiple bridges located in Zhejiang (China), and then built a dataset of bridge damage images. First, experiments were carried out on an open concrete crack dataset. The mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and frames per second (FPS) of the EMA-DenseNet are 87.42%, 92.59%, 81.97% and 25.4, respectively. Then we also conducted experiments on a more challenging bridge damage dataset, the MIoU, where MPA, precision and FPS were 79.87%, 86.35%, 74.70% and 14.6, respectively. Compared with the current state-of-the-art algorithms, the proposed algorithm is more accurate and robust in bridge damage detection.<\/jats:p>","DOI":"10.3390\/s21030824","type":"journal-article","created":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T08:29:16Z","timestamp":1611649756000},"page":"824","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Computer Vision-Based Bridge Damage Detection Using Deep Convolutional Networks with Expectation Maximum Attention Module"],"prefix":"10.3390","volume":"21","author":[{"given":"Wenting","family":"Qiao","sequence":"first","affiliation":[{"name":"School of Highway, Chang\u2019an University, Xi\u2019an 710064, China"},{"name":"Inner Mongolia Transport Construction Engineering Quality Supervision Bureau, Hohhot 010020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6273-3061","authenticated-orcid":false,"given":"Biao","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiangwei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoguang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Highway, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1296-2376","authenticated-orcid":false,"given":"Gang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1002\/rob.21725","article-title":"Development of an autonomous bridge deck inspection robotic system","volume":"34","author":"La","year":"2017","journal-title":"J. Field Robot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.advengsoft.2017.05.009","article-title":"An information modeling framework for bridge monitoring","volume":"114","author":"Jeong","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.ymssp.2017.04.022","article-title":"On switching response surface models, with applications to the structural health monitoring of bridges","volume":"98","author":"Worden","year":"2018","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhou, G., Li, A., Li, J., and Duan, M. (2018). Structural Health Monitoring and Time-Dependent Effects Analysis of Self-Anchored Suspension Bridge with Extra-Wide Concrete Girder. Appl. Sci., 8.","DOI":"10.3390\/app8010115"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1007\/s11227-019-03045-8","article-title":"Data mining algorithms for bridge health monitoring: Kohonen clustering and LSTM pre-diction approaches","volume":"76","author":"Guo","year":"2020","journal-title":"J. Supercomput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1002\/stc.1800","article-title":"Ultrasonic health monitoring in structural engineering: Buildings and bridges","volume":"23","author":"Mutlib","year":"2015","journal-title":"Struct. Control Health Monit."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1002\/stc.1675","article-title":"Localization of acoustic emission sources in structural health monitoring of masonry bridge","volume":"22","author":"Han","year":"2014","journal-title":"Struct. Control Health Monit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s00138-009-0189-8","article-title":"Fast crack detection method for large-size concrete surface images using percolation-based image processing","volume":"21","author":"Yamaguchi","year":"2010","journal-title":"Mach. Vis. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/S0926-5805(02)00012-2","article-title":"The development of a mobile manipulator imaging system for bridge crack inspection","volume":"11","author":"Tung","year":"2002","journal-title":"Autom. Constr."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Akagic, A., Buza, E., Omanovic, S., and Karabegovic, A. (2018, January 21\u201325). Pavement crack detection using Otsu thresholding for image segmentation. Proceedings of the 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO.2018.8400199"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tang, J., and Gu, Y. (2013, January 13\u201316). Automatic crack detection and segmentation using a hybrid algorithm for road distress analysis. Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK.","DOI":"10.1109\/SMC.2013.516"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ronny Salim, L., La, H.M., Zeyong, S., and Weihua, S. (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.","DOI":"10.1109\/ICRA.2011.5980131"},{"key":"ref_13","unstructured":"Kim, J.-W., Kim, S.-B., Park, J.-C., and Nam, J.-W. (2015, January 25\u201329). Development of crack detection system with unmanned aerial vehicles and digital image processing. Proceedings of the 2015 World Congress on Advances in Structural Engineering and Mechanics (ASEM15), Incheon, Korea."},{"key":"ref_14","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":"2015","journal-title":"Struct. Control Health Monit."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.ndteint.2013.04.006","article-title":"An efficient image-based damage detection for cable surface in cable-stayed bridges","volume":"58","author":"Ho","year":"2013","journal-title":"NDT E Int."},{"key":"ref_16","first-page":"283","article-title":"Tuwards UAV-based bridge inspection systems: A reivew and an application perspective","volume":"2","author":"Chan","year":"2015","journal-title":"Struct. Monit. Maint."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, C.H., Wen, M.C., Chen, Y.C., and Kang, S.C. (2015). An optimized unmanned aeiral system for bridge inspection. Proceedings of the Insternational Symposium on Automation and Robotics in Construction, IAARC Publications.","DOI":"10.22260\/ISARC2015\/0084"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dorafshan, S., Maguire, M., Hoffer, N., and Coopmans, C. (2017). Fatigue Crack Detection Using Unmanned Aerial Systems in Un-der-Bridge Inspection, Idaho Transportation Department.","DOI":"10.1061\/(ASCE)BE.1943-5592.0001291"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.advengsoft.2015.02.005","article-title":"Thin crack observation in a reinforced concrete bridge pier test using image processing and analysis","volume":"83","author":"Yang","year":"2015","journal-title":"Adv. Eng. Softw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/14680629.2019.1614969","article-title":"Faster region convolutional neural network for automated pavement distress detection","volume":"22","author":"Song","year":"2021","journal-title":"Road Mater. Pavement Des."},{"key":"ref_21","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_22","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Harrahs and Harveys, Lake Tahoe, NV, USA."},{"key":"ref_23","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.conbuildmat.2018.08.011","article-title":"Comparison of deep convolutional neural networks and edge detectors for im-age-based crack detection in concrete","volume":"186","author":"Dorafshan","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1111\/mice.12297","article-title":"Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network","volume":"32","author":"Zhang","year":"2017","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"04018041","DOI":"10.1061\/(ASCE)CP.1943-5487.0000775","article-title":"Deep Learning\u2013Based Fully Automated Pavement Crack Detection on 3D Asphalt Surfaces with an Improved CrackNet","volume":"32","author":"Zhang","year":"2018","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1111\/mice.12263","article-title":"Deep learning-based crack damage detection using convolutional neural networks","volume":"32","author":"Cha","year":"2017","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1177\/1475921718764873","article-title":"Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images","volume":"18","author":"Xu","year":"2018","journal-title":"Struct. Health Monit."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 26\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NY, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the CVPR 2017, IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_32","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 International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmen-tation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1111\/mice.12412","article-title":"Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network","volume":"33","author":"Yang","year":"2018","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.autcon.2019.04.005","article-title":"Computer vision-based concrete crack detection using U-net fully convolutional net-works","volume":"104","author":"Liu","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"117367","DOI":"10.1016\/j.conbuildmat.2019.117367","article-title":"Image-based concrete crack detection in tunnels using deep fully convolutional networks","volume":"234","author":"Ren","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, G., Ma, B., He, S., Ren, X., and Liu, Q. (2020). Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique. Sensors, 20.","DOI":"10.3390\/s20030717"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4392","DOI":"10.1109\/TIE.2017.2764844","article-title":"NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Na\u00efve Bayes Data Fusion","volume":"65","author":"Chen","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/6412562","article-title":"Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features","volume":"2020","author":"Song","year":"2020","journal-title":"J. Adv. Transp."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201322). Non-local Neural Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_41","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, X., Zhong, Z., Wu, J., Yang, Y., Lin, Z., and Liu, H. (November, January 27). Expectation-maximization attention networks for semantic segmentation. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00926"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1111\/mice.12440","article-title":"Encoder\u2013decoder 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_45","doi-asserted-by":"crossref","first-page":"103018","DOI":"10.1016\/j.autcon.2019.103018","article-title":"Densely connected deep neural network considering connectivity of pixels for automatic crack detection","volume":"110","author":"Mei","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_46","unstructured":"Kingma, D.P., and Ba, J. (2015, January 5\u20138). Adam: A method for stochastic optimization. Proceedings of the International Conference Learn. Represent (ICLR), San Diego, CA, USA."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 8\u201310). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Piscataway, NJ, USA.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"8016","DOI":"10.1109\/TIE.2019.2945265","article-title":"SDDNet: Real-Time Crack Segmentation","volume":"67","author":"Choi","year":"2019","journal-title":"IEEE Trans. Ind. Electron."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/824\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:15:34Z","timestamp":1760159734000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/824"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,26]]},"references-count":49,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21030824"],"URL":"https:\/\/doi.org\/10.3390\/s21030824","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,26]]}}}