{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T15:33:01Z","timestamp":1784734381762,"version":"3.55.0"},"reference-count":49,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,4,7]],"date-time":"2020-04-07T00:00:00Z","timestamp":1586217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFB1310504"],"award-info":[{"award-number":["2019YFB1310504"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Science and Technology Program","award":["2018GZDZX0043"],"award-info":[{"award-number":["2018GZDZX0043"]}]},{"name":"Sichuan Science and Technology Program","award":["2019YFG0144"],"award-info":[{"award-number":["2019YFG0144"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Crack detection on dam surfaces is an important task for safe inspection of hydropower stations. More and more object detection methods based on deep learning are being applied to crack detection. However, most of the methods can only achieve the classification and rough location of cracks. Pixel-level crack detection can provide more intuitive and accurate detection results for dam health assessment. To realize pixel-level crack detection, a method of crack detection on dam surface (CDDS) using deep convolution network is proposed. First, we use an unmanned aerial vehicle (UAV) to collect dam surface images along a predetermined trajectory. Second, raw images are cropped. Then crack regions are manually labelled on cropped images to create the crack dataset, and the architecture of CDDS network is designed. Finally, the CDDS network is trained, validated and tested using the crack dataset. To validate the performance of the CDDS network, the predicted results are compared with ResNet152-based, SegNet, UNet and fully convolutional network (FCN). In terms of crack segmentation, the recall, precision, F-measure and IoU are 80.45%, 80.31%, 79.16%, and 66.76%. The results on test dataset show that the CDDS network has better performance for crack detection of dam surfaces.<\/jats:p>","DOI":"10.3390\/s20072069","type":"journal-article","created":{"date-parts":[[2020,4,8]],"date-time":"2020-04-08T05:59:47Z","timestamp":1586325587000},"page":"2069","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":123,"title":["Automatic Pixel-Level Crack Detection on Dam Surface Using Deep Convolutional Network"],"prefix":"10.3390","volume":"20","author":[{"given":"Chuncheng","family":"Feng","sequence":"first","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Wang","sequence":"additional","affiliation":[{"name":"Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4113-2408","authenticated-orcid":false,"given":"Yonglong","family":"Li","sequence":"additional","affiliation":[{"name":"Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1400","DOI":"10.1002\/tal.1400","article-title":"A novel machine learning-based algorithm to detect damage in high-rise building structures","volume":"26","author":"Rafiei","year":"2017","journal-title":"Struct. Des. Tall Spec. Build."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/TASE.2014.2354314","article-title":"Automated crack detection on concrete bridges","volume":"13","author":"Prasanna","year":"2014","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1177\/1475921718768747","article-title":"Crack and noncrack classification from concrete surface images using machine learning","volume":"18","author":"Kim","year":"2019","journal-title":"Struct. Health Monit."},{"key":"ref_4","first-page":"145","article-title":"Tunnel crack detection and classification system based on image processing","volume":"4664","author":"Liu","year":"2002","journal-title":"Mach. Vis. Appl. Ind. Insp. X Int. Soc. Opt. Photon."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.autcon.2005.02.006","article-title":"Automated detection of cracks in buried concrete pipe images","volume":"15","author":"Sinha","year":"2006","journal-title":"Autom. Constr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1111\/j.1467-8667.2011.00716.x","article-title":"Concrete crack detection by multiple sequential image filtering","volume":"27","author":"Takafumi","year":"2012","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.autcon.2016.06.008","article-title":"Vision-based detection of loosened bolts using the hough transform and support vector machines","volume":"71","author":"Cha","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3434","DOI":"10.1109\/TITS.2016.2552248","article-title":"Automatic road crack detection using random structured forests","volume":"17","author":"Shi","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1111\/mice.12042","article-title":"Road crack detection using visual features extracted by Gabor filters","volume":"29","author":"Zalama","year":"2014","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.autcon.2017.01.019","article-title":"Recognition and evaluation of bridge cracks with modified active contour model and greedy search-based support vector machine","volume":"78","author":"Li","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1002\/tee.20244","article-title":"Image-based crack detection for real concrete surfaces","volume":"3","author":"Yamaguchi","year":"2008","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"60100","DOI":"10.1109\/ACCESS.2018.2875889","article-title":"Image-Based Crack Detection Using Crack Width Transform (CWT) Algorithm","volume":"6","author":"Cho","year":"2018","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1111\/j.1467-8667.2010.00674.x","article-title":"Beamlet transform-based technique for pavement crack detection and classification","volume":"25","author":"Ying","year":"2010","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_14","unstructured":"Ito, A., Aoki, Y., and Hashimoto, S. (2002, January 5\u20138). Accurate extraction and measurement of fine cracks from concrete block surface image. Proceedings of the IEEE 2002 28th Annual Conference of the Industrial Electronics Society, Sevilla, Spain."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.autcon.2013.10.021","article-title":"Long-distance precision inspection method for bridge cracks with image processing","volume":"41","author":"Li","year":"2014","journal-title":"Autom. Constr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"013017","DOI":"10.1117\/1.2177650","article-title":"Automatic inspection of pavement cracking distress","volume":"15","author":"Huang","year":"2006","journal-title":"J. Electron. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Subirats, P., Dumoulin, J., Legeay, V., and Barba, D. (2006, January 8\u201311). Automation of pavement surface crack detection using the continuous wavelet transform. Proceedings of the 2006 International Conference on Image Processing, Atlanta, GA, USA.","DOI":"10.1109\/ICIP.2006.313007"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"04014118","DOI":"10.1061\/(ASCE)CP.1943-5487.0000451","article-title":"Improvement of crack-detection accuracy using a novel crack defragmentation technique in image-based road assessment","volume":"30","author":"Wu","year":"2014","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_20","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., and Rabinovich, A. (2015, January 7\u201312). 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_22","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1111\/mice.12308","article-title":"On-line vehicle routing problems for carbon emissions reduction","volume":"32","author":"Liao","year":"2017","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.conbuildmat.2017.09.110","article-title":"Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection","volume":"157","author":"Gopalakrishnan","year":"2017","journal-title":"Constr. Build. Mater."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Li, L., Zhang, H., Pang, J., and Huang, J. (2019, January 20\u201322). Dam surface crack detection based on deep learning. Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence, Shanghai, China.","DOI":"10.1145\/3366194.3366327"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Protopapadakis, E., Doulamis, A., Doulamis, N., and Loupos, C. (2015, January 3\u20135). Deep convolutional neural networks for efficient vision based tunnel inspection. Proceedings of the 2015 IEEE International Conference on Intelligent Computer Communication and Processing, Cluj-Napoca, Romania.","DOI":"10.1109\/ICCP.2015.7312681"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.autcon.2018.07.008","article-title":"Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network","volume":"94","author":"Nguyen","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Feng, C., Liu, M.Y., Kao, C.C., and Lee, T.Y. (2017, January 25\u201327). Deep active learning for civil infrastructure defect detection and classification. Proceedings of the ASCE International Workshop on Computing in Civil Engineering, Seattle, WA, USA.","DOI":"10.1061\/9780784480823.036"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s13349-018-0285-4","article-title":"Bridge inspection: Human performance, unmanned aerial systems and automation","volume":"8","author":"Dorafshan","year":"2018","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Khaloo, A., Lattanzi, D., Jachimowicz, A., and Devaney, C. (2018). Utilizing UAV and 3D computer vision for visual inspection of a large gravity dam. Front. Built Environ., 4.","DOI":"10.3389\/fbuil.2018.00031"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dorafshan, S., Thomas, R.J., Coopmans, C., and Maguire, M. (2018, January 12\u201315). Deep learning neural networks for sUAS-assisted structural inspections: Feasibility and application. Proceedings of the 2018 International Conference on Unmanned Aircraft Systems (ICUAS), Dallas, TX, USA.","DOI":"10.1109\/ICUAS.2018.8453409"},{"key":"ref_32","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_33","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_34","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_35","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. Aided Civ. Infrastruct. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1111\/mice.12351","article-title":"Unified vision-based methodology for simultaneous concrete defect detection and geolocalization","volume":"33","author":"Li","year":"2018","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_37","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":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_38","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. Aided Civ. Infrastruct. Eng."},{"key":"ref_39","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_40","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_41","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_42","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhao, H., Qi, X., Shen, X., Shi, J., and Jia, J. (2018). Icnet for real-time semantic segmentation on high-resolution images. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-01219-9_25"},{"key":"ref_44","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/TIP.2018.2878966","article-title":"Deepcrack: Learning hierarchical convolutional features for crack detection","volume":"28","author":"Zou","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted Boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1145\/357994.358023","article-title":"A fast parallel algorithm for thinning digital patterns","volume":"27","author":"Zhang","year":"1984","journal-title":"Commun. ACM"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/2069\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:16:09Z","timestamp":1760174169000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/2069"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,7]]},"references-count":49,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["s20072069"],"URL":"https:\/\/doi.org\/10.3390\/s20072069","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,7]]}}}