{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T16:25:33Z","timestamp":1786379133360,"version":"build-2736575974"},"reference-count":31,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12072279"],"award-info":[{"award-number":["12072279"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11372256"],"award-info":[{"award-number":["11372256"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11527803"],"award-info":[{"award-number":["11527803"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11602201"],"award-info":[{"award-number":["11602201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11502216"],"award-info":[{"award-number":["11502216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11602202"],"award-info":[{"award-number":["11602202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Program of Shaanxi Province","doi-asserted-by":"publisher","award":["2018JQ1060"],"award-info":[{"award-number":["2018JQ1060"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fatigue failure is a significant problem in the structural safety of engineering structures. Human inspection is the most widely used approach for fatigue failure detection, which is time consuming and subjective. Traditional vision-based methods are insufficient in distinguishing cracks from noises and detecting crack tips. In this paper, a new framework based on convolutional neural networks (CNN) and digital image processing is proposed to monitor crack propagation length. Convolutional neural networks were first applied to robustly detect the location of cracks with the interference of scratch and edges. Then, a crack tip-detection algorithm was established to accurately locate the crack tip and was used to calculate the length of the crack. The effectiveness and precision of the proposed approach were validated through conducting fatigue experiments. The results demonstrated that the proposed approach could robustly identify a fatigue crack surrounded by crack-like noises and locate the crack tip accurately. Furthermore, crack length could be measured with submillimeter accuracy.<\/jats:p>","DOI":"10.3390\/s21175894","type":"journal-article","created":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T23:05:12Z","timestamp":1630623912000},"page":"5894","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Crack Length Measurement Using Convolutional Neural Networks and Image Processing"],"prefix":"10.3390","volume":"21","author":[{"given":"Yingtao","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"International Research Laboratory of Impact Dynamics and its Engineering Application, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhendong","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"International Research Laboratory of Impact Dynamics and its Engineering Application, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Su","sequence":"additional","affiliation":[{"name":"School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"International Research Laboratory of Impact Dynamics and its Engineering Application, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"International Research Laboratory of Impact Dynamics and its Engineering Application, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Suo","sequence":"additional","affiliation":[{"name":"School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"International Research Laboratory of Impact Dynamics and its Engineering Application, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Optical Measurement and Detection, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifeng","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"International Research Laboratory of Impact Dynamics and its Engineering Application, Xi\u2019an 710072, China"},{"name":"Institute of Intelligent Optical Measurement and Detection, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.isprsjprs.2009.02.004","article-title":"Crack measurement: Development, testing and applications of an automatic image-based algorithm","volume":"64","author":"Barazzetti","year":"2009","journal-title":"ISPRS J. Photogramm."},{"key":"ref_2","first-page":"3111","article-title":"Fatigue crack length real time measurement method based on camera automatically tracking and positioning","volume":"130","author":"Gao","year":"2012","journal-title":"Appl. Mech. Mater."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.ijfatigue.2016.06.023","article-title":"A modified compliance method for fatigue crack propagation applied on a single edge notch specimen","volume":"92","author":"Ewest","year":"2016","journal-title":"Int. J. Fatigue"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e2332","DOI":"10.1002\/stc.2332","article-title":"Experimental study on acoustic emission characteristic of fatigue crack growth of self-compacting concrete","volume":"26","author":"Chen","year":"2019","journal-title":"Struct. Control. Health Monit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"121089","DOI":"10.1016\/j.conbuildmat.2020.121089","article-title":"A review on acoustic emission monitoring for damage detection in masonry structures","volume":"268","author":"Verstrynge","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/j.ijmst.2020.06.007","article-title":"Dynamic tensile behaviour and crack propagation of coal under coupled static-dynamic loading","volume":"30","author":"Hao","year":"2020","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s40799-016-0094-9","article-title":"Automatic optical crack tracking for double cantilever beam specimens","volume":"40","author":"Krull","year":"2016","journal-title":"Exp. Tech."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1007\/s11668-018-0493-6","article-title":"Research on fatigue crack growth detection of M (T) specimen based on image processing technology","volume":"18","author":"Sun","year":"2018","journal-title":"J. Fail. Anal. Prev."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1111\/mice.12353","article-title":"Vision-based fatigue crack detection of steel structures using video feature tracking","volume":"33","author":"Kong","year":"2018","journal-title":"Comput.-Aided Civ. Inf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.autcon.2018.12.011","article-title":"Non-contact fatigue crack detection in civil infrastructure through image overlapping and crack breathing sensing","volume":"99","author":"Kong","year":"2019","journal-title":"Automat. Constr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107166","DOI":"10.1016\/j.engfracmech.2020.107166","article-title":"Experimental study on fracture and fatigue crack propagation processes in concrete based on DIC technology","volume":"235","author":"Li","year":"2020","journal-title":"Eng. Fract. Mech."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.optlaseng.2008.03.016","article-title":"A digital image correlation method for fatigue test experiments","volume":"47","author":"Vanlanduit","year":"2009","journal-title":"Opt. Laser. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.measurement.2012.07.019","article-title":"Automatic crack monitoring using photogrammetry and image processing","volume":"46","author":"Costa","year":"2013","journal-title":"Measurement"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1111\/mice.12141","article-title":"Vision-based automated crack detection for bridge inspection","volume":"30","author":"Yeum","year":"2015","journal-title":"Comput.-Aided Civ. Inf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.1007\/s11771-020-4530-8","article-title":"Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform","volume":"27","author":"Dong","year":"2020","journal-title":"J. Cent. S. Univ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1177\/1475921718804132","article-title":"A novel deep learning-based method for damage identification of smart building structures","volume":"18","author":"Yu","year":"2019","journal-title":"Struct. Health Monit."},{"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":"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_20","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_21","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s40799-020-00425-1","article-title":"Crack propagation detection method in the structural fatigue process","volume":"45","author":"Guo","year":"2021","journal-title":"Exp. Tech."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Berg, A. (2016, January 8\u201316). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision\u2014ECCV, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_23","unstructured":"Howard, A., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_24","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the Conference Neural Information Processing Systems (NIPS), Lake Tahoe, NV, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., and Guadarrama, S. (2017, January 22\u201325). Speed\/accuracy trade-offs for modern convolutional object detectors. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.351"},{"key":"ref_26","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 Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_27","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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","unstructured":"Pitas, I. (2000). Digital Image Processing Algorithms and Applications, Wiley & Sons."},{"key":"ref_29","unstructured":"Tzutalin, D. (2018, February 01). LabelImg. Git Code. Available online: https:\/\/github.com\/tzutalin\/labelImg."},{"key":"ref_30","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014, January 8\u201313). How transferable are features in deep neural networks?. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Oquab, M., Bottou, L., Laptev, I., and Sivic, J. (2014, January 23\u201328). Learning and transferring mid-level image representations using convolutional neural networks. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.222"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/17\/5894\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:54:28Z","timestamp":1760165668000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/17\/5894"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,1]]},"references-count":31,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["s21175894"],"URL":"https:\/\/doi.org\/10.3390\/s21175894","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,1]]}}}