{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T01:04:15Z","timestamp":1776387855504,"version":"3.51.2"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T00:00:00Z","timestamp":1580169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["300102329203"],"award-info":[{"award-number":["300102329203"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of      496 \u00a0 \u00d7 \u00a0 496      pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder\u2013decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it\u2019s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from \u221211.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.<\/jats:p>","DOI":"10.3390\/s20030717","type":"journal-article","created":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T09:37:09Z","timestamp":1580204229000},"page":"717","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1296-2376","authenticated-orcid":false,"given":"Gang","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, Shaanxi, China"},{"name":"Key Laboratory of Road Construction Technology and Equipment of MOE, Chang\u2019an University, Xi\u2019an 710064, Shaanxi, 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, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuanhai","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory for Old Bridge Detection and Reinforcement Technology of Ministry of Transportation, Chang\u2019an University, Xi\u2019an 710064, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueli","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, Shaanxi, 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, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,28]]},"reference":[{"key":"ref_1","unstructured":"Zhang, D., and Huang, X. (2018). Study on the Grade Evaluation of Highway Tunnel Cracks Based on PFC Simulation and BP Neural Network. Proceedings of GeoShanghai 2018 International Conference: Tunnelling and Underground Construction, Springer Singapore."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.autcon.2006.05.003","article-title":"Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel","volume":"16","author":"Yu","year":"2007","journal-title":"Automat. Constr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1139\/l07-008","article-title":"Development of an inspection system for cracks in a concrete tunnel lining","volume":"34","author":"Lee","year":"2007","journal-title":"Can. J. Civ. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.autcon.2015.02.003","article-title":"Past, present and future of robotic tunnel inspection","volume":"59","author":"Montero","year":"2015","journal-title":"Automat. Constr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"184639","DOI":"10.1155\/2015\/184639","article-title":"Wireless Multimedia Sensor Network Based Subway Tunnel Crack Detection Method","volume":"11","author":"Shen","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"19307","DOI":"10.3390\/s141019307","article-title":"Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring","volume":"14","author":"Zhang","year":"2014","journal-title":"Sensors"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1061\/(ASCE)0887-3801(2003)17:4(255)","article-title":"Analysis of Edge-Detection Techniques for Crack Identification in Bridges","volume":"17","author":"Abudayyeh","year":"2003","journal-title":"J Comput Civil Eng."},{"key":"ref_9","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 image-based crack detection in concrete","volume":"186","author":"Dorafshan","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_10","unstructured":"Oliveira, H., and Correia, P.L. (2009, January 24\u201328). Automatic road crack segmentation using entropy and image dynamic thresholding. Proceedings of the 17th European Signal Processing Conference, Glasgow, Scotland."},{"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","first-page":"140","DOI":"10.13176\/11.167","article-title":"A Novel LBP Based Methods for Pavement Crack Detection","volume":"5","author":"Hu","year":"2010","journal-title":"J. Pattern Recognit. Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Akagic, A., Buza, E., Omanovic, S., and Karabegovic, A. (May, January ). 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_14","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1109\/TITS.2018.2856928","article-title":"Automatic pavement crack detection by multi-scale image fusion","volume":"20","author":"Li","year":"2018","journal-title":"Ieee T Intell Transp"},{"key":"ref_15","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 Aided Civ Inf."},{"key":"ref_16","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 Civil Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"04018001","DOI":"10.1061\/(ASCE)CP.1943-5487.0000736","article-title":"Unified approach to pavement crack and sealed crack detection using preclassification based on transfer learning","volume":"32","author":"Zhang","year":"2018","journal-title":"J Comput Civil Eng."},{"key":"ref_18","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 inf."},{"key":"ref_19","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":"2019","journal-title":"Struct. Health Monit."},{"key":"ref_20","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 T Ind Electron."},{"key":"ref_21","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 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W., and Frangi, A. (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\/978-3-319-24571-3"},{"key":"ref_23","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. Inf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.tust.2018.04.002","article-title":"Deep learning based image recognition for crack and leakage defects of metro shield tunnel","volume":"77","author":"Huang","year":"2018","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_25","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 networks","volume":"104","author":"Liu","year":"2019","journal-title":"Automat. Constr."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhong, Z., Shen, T., and Lin, Z. (2018, January 18\u201322). Convolutional neural networks with alternately updated clique. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00256"},{"key":"ref_27","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_28","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the thirteenth international conference on artificial intelligence and statistics, Chia Laguna, Sardinia, Italy."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE international conference on computer vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_30","unstructured":"Sutskever, I., Martens, J., Dahl, G., and Hinton, G. (2013, January 16\u201321). On the importance of initialization and momentum in deep learning. Proceedings of the International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_31","unstructured":"Cotter, A., Shamir, O., Srebro, N., and Sridharan, K. (2011, January 12\u201317). Better mini-batch algorithms via accelerated gradient methods. Proceedings of the 24th International Conference on Neural Information Processing Systems (NIPS\u201911), Red Hook, NY, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bengio, Y. (2012). Practical recommendations for gradient-based training of deep architectures. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"ref_33","unstructured":"Wager, S., Wang, S., and Liang, P.S. (2013, January 5\u201310). Dropout training as adaptive regularization. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_34","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv."},{"key":"ref_35","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., and Garcia-Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_37","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":"Automat. Constr."},{"key":"ref_38","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. Inf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/717\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:20:46Z","timestamp":1760361646000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/717"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,28]]},"references-count":38,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20030717"],"URL":"https:\/\/doi.org\/10.3390\/s20030717","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,28]]}}}