{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:28:09Z","timestamp":1781368089907,"version":"3.54.1"},"reference-count":30,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shaanxi Provincial Key Laboratory of Land Consolidation","award":["2019-ZY01"],"award-info":[{"award-number":["2019-ZY01"]}]},{"name":"Shaanxi Provincial Key Laboratory of Land Consolidation","award":["2021SF2-02"],"award-info":[{"award-number":["2021SF2-02"]}]},{"name":"Shaanxi Provincial Key Laboratory of Land Consolidation","award":["202109"],"award-info":[{"award-number":["202109"]}]},{"name":"Shaanxi Provincial Key R&amp;D Program","award":["2019-ZY01"],"award-info":[{"award-number":["2019-ZY01"]}]},{"name":"Shaanxi Provincial Key R&amp;D Program","award":["2021SF2-02"],"award-info":[{"award-number":["2021SF2-02"]}]},{"name":"Shaanxi Provincial Key R&amp;D Program","award":["202109"],"award-info":[{"award-number":["202109"]}]},{"name":"Shaanxi Provincial Public Welfare Geology Project","award":["2019-ZY01"],"award-info":[{"award-number":["2019-ZY01"]}]},{"name":"Shaanxi Provincial Public Welfare Geology Project","award":["2021SF2-02"],"award-info":[{"award-number":["2021SF2-02"]}]},{"name":"Shaanxi Provincial Public Welfare Geology Project","award":["202109"],"award-info":[{"award-number":["202109"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the increase in urban rail transit construction, instances of tunnel disease are on the rise, and cracks have become the focus of tunnel maintenance and management. Therefore, it is essential to carry out crack detection in a timely and efficient manner to not only prolong the service life of the tunnel but also reduce the incidence of accidents. In this paper, the design and structure of a tunnel crack detection system are analyzed. On this basis, this paper proposes a new method for crack identification and feature detection using image processing technology. This method fully considers the characteristics of tunnel images and the combination of these characteristics with deep learning, while a deep convolutional network (Single-Shot MultiBox Detector (SSD)) is proposed based on deep learning for object detection in complex images. The experimental results show that the test set accuracy and training set accuracy of the support vector machine (SVM) in the classification comparison test are up to 88% and 87.8%, respectively; while the test accuracy of Alexnet\u2019s deep convolutional neural network-based classification and identification is up to 96.7%, and the training set accuracy is up to 97.5%. It can be seen that this deep convolutional network recognition algorithm based on deep learning and image processing is better and more suitable for the detection of cracks in subway tunnels.<\/jats:p>","DOI":"10.3390\/s23136070","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:53:16Z","timestamp":1688345596000},"page":"6070","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Image-Processing-Based Subway Tunnel Crack Detection System"],"prefix":"10.3390","volume":"23","author":[{"given":"Xiaofeng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Land Engineering, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zenglin","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Land Engineering, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Shaanxi Province Institute of Geological Survey, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Shi","sequence":"additional","affiliation":[{"name":"Shaanxi Hydrogeology Engineering Geology and Environment Geology Survey Center, Xi\u2019an 710068, China"},{"name":"Shaanxi Engineering Technology Research Center for Urban Geology and Underground Space, Xi\u2019an 710068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodan","family":"Guo","sequence":"additional","affiliation":[{"name":"Shaanxi Province Institute of Geological Survey, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fu, H., Deng, H., Zhao, Y., Chang, X., and Yi, H. 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