{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T22:35:12Z","timestamp":1792535712732,"version":"4.5.0"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2014,10,16]],"date-time":"2014-10-16T00:00:00Z","timestamp":1413417600000},"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>Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS) industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification.<\/jats:p>","DOI":"10.3390\/s141019307","type":"journal-article","created":{"date-parts":[[2014,10,16]],"date-time":"2014-10-16T10:34:40Z","timestamp":1413455680000},"page":"19307-19328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":263,"title":["Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring"],"prefix":"10.3390","volume":"14","author":[{"given":"Wenyu","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0217-3012","authenticated-orcid":false,"given":"Zhenjiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dapeng","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2014,10,16]]},"reference":[{"key":"ref_1","unstructured":"Basler Industrial Cameras\u2014racer Series\u2014raL12288-66km. Available online: http:\/\/www.baslerweb.com\/products\/racer.html?model=370."},{"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":"Autom. Constr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9628","DOI":"10.3390\/s111009628","article-title":"Adaptive Road Crack Detection System by Pavement Classification","volume":"11","author":"Balcones","year":"2011","journal-title":"Sensors"},{"key":"ref_4","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_5","unstructured":"Chen, S.E., Liu, W., Bian, H., and Smith, B. (2014). 3D LiDAR Scans for Bridge Damage Evaluations. Bridges."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ndteint.2013.11.001","article-title":"Crack detection limits in unit based masonry with terrestrial laser scanning","volume":"62","author":"Laefer","year":"2014","journal-title":"NDT E Int."},{"key":"ref_7","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. Civ. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.engstruct.2003.10.008","article-title":"Damage detection of structures by wavelet analysis","volume":"26","author":"Kim","year":"2004","journal-title":"Eng. Struct."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1177\/1475921706067741","article-title":"Wavelet transform for structural health monitoring: A compendium of uses and features","volume":"5","author":"Taha","year":"2006","journal-title":"Struct. Health Monit."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2299","DOI":"10.1016\/j.ijsolstr.2005.07.024","article-title":"Wavelet-based crack identification of bridge beam from operational deflection time history","volume":"43","author":"Zhu","year":"2006","journal-title":"Int. J. Solids Struct."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/TIP.2005.860311","article-title":"Digital image processing techniques for the detection and removal of cracks in digitized paintings","volume":"15","author":"Giakoumis","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","first-page":"11","article-title":"Detecting cracks on the tunnel wall using watershed and graph analysis","volume":"29","author":"Miwa","year":"2005","journal-title":"ITE Tech. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s00138-005-0012-0","article-title":"Morphological segmentation and classification of underground pipe images","volume":"17","author":"Sinha","year":"2006","journal-title":"Mach Vis. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1109\/JSTSP.2012.2212416","article-title":"Morphology-based crack detection for steel slabs","volume":"6","author":"Landstrom","year":"2012","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fujita, Y., and Hamamoto, Y. (2009, January 24\u201327). A robust method for automatically detecting cracks on noisy concrete surfaces. Tainan, Taiwan.","DOI":"10.1007\/978-3-642-02568-6_8"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.autcon.2005.02.005","article-title":"Neuro-fuzzy network for the classification of buried pipe defects","volume":"15","author":"Sinha","year":"2006","journal-title":"Autom. Constr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.autcon.2011.11.018","article-title":"Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures","volume":"22","author":"Jahanshahi","year":"2012","journal-title":"Autom. Constr."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1016\/j.advengsoft.2006.06.002","article-title":"PCA-based algorithm for unsupervised bridge crack detection","volume":"37","author":"Abudayyeh","year":"2006","journal-title":"Adv. Eng. Softw."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dougherty, E.R., and Lotufo, R.A. (2003). Hands-on Morphological Image Processing, Spie.","DOI":"10.1117\/3.501104"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.optlastec.2011.07.009","article-title":"Image enhancement using multi scale image features extracted by top-hat transform","volume":"44","author":"Bai","year":"2012","journal-title":"Opt. Laser Technol."},{"key":"ref_22","first-page":"23","article-title":"A threshold selection method from gray-level histograms","volume":"11","author":"Otsu","year":"1975","journal-title":"Automatica"},{"key":"ref_23","unstructured":"Soille, P. (2003). Morphological Image Analysis: Principles and Applications, Springer-Verlag New York, Inc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s11947-011-0556-0","article-title":"Shape analysis of agricultural products: A review of recent research advances and potential application to computer vision","volume":"4","author":"Costa","year":"2011","journal-title":"Food Bioprocess Technol."},{"key":"ref_25","first-page":"625","article-title":"Crack damage detection in beam-like structures using RBF neural networks with experimental validation","volume":"1","author":"Li","year":"2005","journal-title":"Int. J. Innov. Comput. Inf. Control"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme learning machine for regression and multiclass classification","volume":"42","author":"Huang","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. 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Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/10\/19307\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:17:00Z","timestamp":1760217420000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/10\/19307"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,10,16]]},"references-count":29,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2014,10]]}},"alternative-id":["s141019307"],"URL":"https:\/\/doi.org\/10.3390\/s141019307","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,10,16]]}}}