{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:30:55Z","timestamp":1760232655516,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T00:00:00Z","timestamp":1668729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"JSPS KAKENHI","doi-asserted-by":"publisher","award":["JP20K19856"],"award-info":[{"award-number":["JP20K19856"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Distresses, such as cracks, directly reflect the structural integrity of subway tunnels. Therefore, the detection of subway tunnel distress is an essential task in tunnel structure maintenance. This paper presents the performance improvement of deep learning-based distress detection to support the maintenance of subway tunnels through a new data augmentation method, selective image cropping and patching (SICAP). Specifically, we generate effective data for training the distress detection model by focusing on the distressed regions via SICAP. After the data augmentation, we train a distress detection model using the expanded training data. The new image generated based on SICAP does not change the pixel values of the original image. Thus, there is little loss of information, and the generated images are effective in constructing a robust model for various subway tunnel lines. We conducted experiments with some comparative methods. The experimental results show that the detection performance can be improved by our data augmentation.<\/jats:p>","DOI":"10.3390\/s22228932","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T06:22:28Z","timestamp":1668752548000},"page":"8932","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Distress Detection in Subway Tunnel Images via Data Augmentation Based on Selective Image Cropping and Patching"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8039-3462","authenticated-orcid":false,"given":"Keisuke","family":"Maeda","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saya","family":"Takada","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomoki","family":"Haruyama","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4474-3995","authenticated-orcid":false,"given":"Ren","family":"Togo","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5332-8112","authenticated-orcid":false,"given":"Takahiro","family":"Ogawa","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miki","family":"Haseyama","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19165","DOI":"10.1109\/JSEN.2021.3089718","article-title":"Automatic pixel-level crack detection for civil infrastructure using Unet++ and deep transfer learning","volume":"21","author":"Yang","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"103487","DOI":"10.1016\/j.tust.2020.103487","article-title":"Deterioration mapping in subway infrastructure using sensory data of GPR","volume":"103","author":"Dawood","year":"2020","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_4","first-page":"57","article-title":"Building durable structures in the 21st century","volume":"23","author":"Mehta","year":"2001","journal-title":"Concr. Int."},{"key":"ref_5","unstructured":"Ministry of Land, Infrastructure Transport and Tourism (2022, November 13). White Paper on Land, Infrastructure, Transport and Tourism in Japan, 2017 (online), Available online: http:\/\/www.mlit.go.jp\/common\/001269888.pdf."},{"key":"ref_6","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":"Autom. Constr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.cemconres.2015.01.010","article-title":"Modeling of concrete cracking due to corrosion process of reinforcement bars","volume":"71","author":"Bossio","year":"2015","journal-title":"Cem. Concr. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"293","DOI":"10.18770\/KEPCO.2016.02.02.293","article-title":"Development of the corrosion deterioration inspection tool for transmission tower members","volume":"2","author":"Woo","year":"2016","journal-title":"KEPCO J. Electr. Power Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JPETS.2015.2395388","article-title":"LineScout technology opens the way to robotic inspection and maintenance of high-voltage power lines","volume":"2","author":"Pouliot","year":"2015","journal-title":"IEEE Power Energy Technol. Syst. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Te\u0161i\u0107, K., Bari\u010devi\u0107, A., and Serdar, M. (2021). Non-destructive corrosion inspection of reinforced concrete using ground-penetrating radar: A review. Materials, 14.","DOI":"10.3390\/ma14040975"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1002\/qre.1634","article-title":"Data analysis for condition-based railway infrastructure maintenance","volume":"31","author":"Bergquist","year":"2015","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1111\/mice.12067","article-title":"Optimizing the alignment of inspection data from track geometry cars","volume":"30","author":"Xu","year":"2015","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.autcon.2018.07.006","article-title":"Damage detection and quantitative analysis of shield tunnel structure","volume":"94","author":"Huang","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6844","DOI":"10.1109\/JSEN.2019.2911015","article-title":"Learning visual similarity for inspecting defective railway fasteners","volume":"19","author":"Liu","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ogawa, N., Maeda, K., Ogawa, T., and Haseyama, M. (2021, January 19\u201322). Correlation-aware attention branch network using multi-modal data for deterioration level estimation of infrastructures. Proceedings of the IEEE International Conference on Image Processing, Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506551"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"65234","DOI":"10.1109\/ACCESS.2021.3074019","article-title":"Distress image retrieval for infrastructure maintenance via self-Trained deep metric learning using experts\u2019 knowledge","volume":"9","author":"Ogawa","year":"2021","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Calder\u00f3n, L.S., and Bair\u00e1n, J. (2017, January 7\u20138). Crack detection in concrete elements from RGB pictures using modified line detection kernels. Proceedings of the Intelligent Systems Conference (IntelliSys), London, UK.","DOI":"10.1109\/IntelliSys.2017.8324222"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.imavis.2016.11.018","article-title":"An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection","volume":"57","author":"Zhang","year":"2017","journal-title":"Image Vis. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s11045-016-0461-9","article-title":"Automatic crack detection from 2D images using a crack measure-based B-spline level set model","volume":"29","author":"Nguyen","year":"2018","journal-title":"Multidimens. Syst. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.conbuildmat.2019.01.150","article-title":"A fast adaptive crack detection algorithm based on a double-edge extraction operator of FSM","volume":"204","author":"Luo","year":"2019","journal-title":"Constr. Build. Mater."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1111\/mice.12428","article-title":"Foreground\u2013background separation technique for crack detection","volume":"34","author":"Nayyeri","year":"2019","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2718","DOI":"10.1109\/TITS.2015.2477675","article-title":"Automatic crack detection on two-dimensional pavement images: An algorithm based on minimal path selection","volume":"17","author":"Amhaz","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1080\/13682199.2016.1146816","article-title":"The algorithm of accelerated cracks detection and extracting skeleton by direction chain code in concrete surface image","volume":"64","author":"Qu","year":"2016","journal-title":"Imaging Sci. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"24452","DOI":"10.1109\/ACCESS.2018.2829347","article-title":"Automatic pixel-level pavement crack detection using information of multi-scale neighborhoods","volume":"6","author":"Ai","year":"2018","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hadjidemetriou, G.M., Christodoulou, S.E., and Vela, P.A. (2016, January 18\u201320). Automated detection of pavement patches utilizing support vector machine classification. Proceedings of the Mediterranean Electrotechnical Conference (MELECON), Limassol, Cyprus.","DOI":"10.1109\/MELCON.2016.7495460"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.autcon.2018.06.017","article-title":"Adaptive wavelet neural network for terrestrial laser scanner-based crack detection","volume":"94","author":"Turkan","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2635","DOI":"10.1109\/JSEN.2019.2952857","article-title":"A machine learning approach to road surface anomaly assessment using smartphone sensors","volume":"20","author":"Basavaraju","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1111\/mice.12451","article-title":"Convolutional sparse coding-based deep random vector functional link network for distress classification of road structures","volume":"34","author":"Maeda","year":"2019","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1109\/JSTSP.2018.2849593","article-title":"Estimation of deterioration levels of transmission towers via deep learning maximizing canonical correlation between heterogeneous features","volume":"12","author":"Maeda","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ogawa, N., Maeda, K., Ogawa, T., and Haseyama, M. (2022). Deterioration level estimation based on convolutional neural network using confidence-aware attention mechanism for infrastructure inspection. Sensors, 22.","DOI":"10.3390\/s22010382"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Maeda, K., Takahashi, S., Ogawa, T., and Haseyama, M. (2019, January 22\u201325). Neural network maximizing ordinally supervised multi-view canonical correlation for deterioration level estimation. Proceedings of the IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803038"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"26581","DOI":"10.1007\/s11042-018-5880-1","article-title":"A novel automatic dam crack detection algorithm based on local-global clustering","volume":"77","author":"Fan","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Guo, X., and Hao, P. (2021). Using a random forest model to predict the location of potential damage on asphalt pavement. Appl. Sci., 11.","DOI":"10.3390\/app112110396"},{"key":"ref_35","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 26\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_37","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 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, A., Togo, R., Ogawa, T., and Haseyama, M. (2019, January 15\u201318). Detection of distress region from subway tunnel images via U-net-based deep semantic segmentation. Proceedings of the IEEE 8th Global Conference on Consumer Electronics (GCCE), Osaka, Japan.","DOI":"10.1109\/GCCE46687.2019.9015391"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.aei.2015.01.008","article-title":"A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure","volume":"29","author":"Koch","year":"2015","journal-title":"Adv. Eng. Inform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.aei.2011.08.006","article-title":"Advances and challenges in computing in civil and building engineering","volume":"25","author":"Tizani","year":"2011","journal-title":"Adv. Eng. Inform."},{"key":"ref_41","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_42","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_43","doi-asserted-by":"crossref","unstructured":"Khoa, N.L.D., Anaissi, A., and Wang, Y. (2017, January 6\u201310). Smart infrastructure maintenance using incremental tensor analysis. Proceedings of the ACM Conference on Information and Knowledge Management, Singapore.","DOI":"10.1145\/3132847.3132851"},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"e2776","DOI":"10.1002\/stc.2776","article-title":"Automatic subway tunnel crack detection system based on line scan camera","volume":"28","author":"Gong","year":"2021","journal-title":"Struct. Control Health Monit."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"\u00d6zgenel, \u00c7.F., and Sorgu\u00e7, A.G. (2018, January 20\u201325). Performance comparison of pretrained convolutional neural networks on crack detection in buildings. Proceedings of the International Symposium on Automation and Robotics in Construction, Berlin, Germany.","DOI":"10.22260\/ISARC2018\/0094"},{"key":"ref_47","first-page":"65","article-title":"A note on retrieval of visually similar distress regions in subway tunnel images: Introduction of deep features extracted by semantic segmentation network","volume":"119","author":"Li","year":"2020","journal-title":"IEICE Tech. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (2017). mixup: Beyond empirical risk minimization. arXiv.","DOI":"10.1007\/978-1-4899-7687-1_79"},{"key":"ref_49","unstructured":"Inoue, H. (2018). Data augmentation by pairing samples for images classification. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2917","DOI":"10.1109\/TCSVT.2019.2935128","article-title":"Data augmentation using random image cropping and patching for deep cnns","volume":"30","author":"Takahashi","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_52","first-page":"1","article-title":"A note on improving performance of deep learning-based distress detection for supporting maintenance of subway tunnels Accuracy verification focusing on tunnel wall characteristics","volume":"120","author":"Haruyama","year":"2021","journal-title":"ITE Tech. Rep."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8932\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:21:05Z","timestamp":1760145665000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8932"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,18]]},"references-count":55,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22228932"],"URL":"https:\/\/doi.org\/10.3390\/s22228932","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,11,18]]}}}