{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T05:04:02Z","timestamp":1780981442105,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:00:00Z","timestamp":1704931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52308323"],"award-info":[{"award-number":["52308323"]}]},{"name":"National Natural Science Foundation of China","award":["BK20220502"],"award-info":[{"award-number":["BK20220502"]}]},{"name":"National Natural Science Foundation of China","award":["ZXL2022488"],"award-info":[{"award-number":["ZXL2022488"]}]},{"name":"Natural Science Foundation of Jiangsu Province, China","award":["52308323"],"award-info":[{"award-number":["52308323"]}]},{"name":"Natural Science Foundation of Jiangsu Province, China","award":["BK20220502"],"award-info":[{"award-number":["BK20220502"]}]},{"name":"Natural Science Foundation of Jiangsu Province, China","award":["ZXL2022488"],"award-info":[{"award-number":["ZXL2022488"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["52308323"],"award-info":[{"award-number":["52308323"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["BK20220502"],"award-info":[{"award-number":["BK20220502"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["ZXL2022488"],"award-info":[{"award-number":["ZXL2022488"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Crack detection plays a critical role in ensuring road safety and maintenance. Traditional, manual, and semi-automatic detection methods have proven inefficient. Nowadays, the emergence of deep learning techniques has opened up new possibilities for automatic crack detection. However, there are few methods with both localization and segmentation abilities, and most perform poorly. The consistent nature of pavement over a small mileage range gives us the opportunity to make improvements. A novel data-augmentation strategy called CrackMover, specifically tailored for crack detection methods, is proposed. Experiments demonstrate the effectiveness of CrackMover for various methods. Moreover, this paper presents a new instance segmentation method for crack detection. It adopts a redesigned backbone network and incorporates a cascade structure for the region-based convolutional network (R-CNN) part. The experimental evaluation showcases significant performance improvements achieved by these approaches in crack detection. The proposed method achieves an average precision of 33.3%, surpassing Mask R-CNN with a Residual Network 50 backbone by 8.6%, proving its effectiveness in detecting crack distress.<\/jats:p>","DOI":"10.3390\/s24020446","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T05:24:12Z","timestamp":1704950652000},"page":"446","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["An Automated Instance Segmentation Method for Crack Detection Integrated with CrackMover Data Augmentation"],"prefix":"10.3390","volume":"24","author":[{"given":"Mian","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Rail Transportation, Soochow University, Suzhou 215006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Rail Transportation, Soochow University, Suzhou 215006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5784-4277","authenticated-orcid":false,"given":"Xiaohua","family":"Bao","sequence":"additional","affiliation":[{"name":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0880-579X","authenticated-orcid":false,"given":"Xiangsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111219","DOI":"10.1016\/j.measurement.2022.111219","article-title":"Crack detection algorithm based on generative adversarial network and convolutional neural network under small samples","volume":"196","author":"Xu","year":"2022","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, X., Jin, W., Xu, X., and Yang, H. (2022). A Domain-Adversarial Multi-Graph Convolutional Network for Unsupervised Domain Adaptation Rolling Bearing Fault Diagnosis. Symmetry, 14.","DOI":"10.3390\/sym14122654"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Subirats, P., Dumoulin, J., Legeay, V., and Barba, D. (2006, January 8\u201311). Automation of pavement surface crack detection using the continuous wavelet transform. Proceedings of the 2006 International Conference on Image Processing, Atlanta, GA, USA.","DOI":"10.1109\/ICIP.2006.313007"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"027007","DOI":"10.1117\/1.2172917","article-title":"Wavelet-based pavement distress detection and evaluation","volume":"45","author":"Zhou","year":"2006","journal-title":"Opt. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Quan, Y., Sun, J., Zhang, Y., and Zhang, H. (2019, January 4\u20137). The method of the road surface crack detection by the improved Otsu threshold. Proceedings of the 2019 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China.","DOI":"10.1109\/ICMA.2019.8816422"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fujita, Y., Mitani, Y., and Hamamoto, Y. (2006, January 20\u201324). A method for crack detection on a concrete structure. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.98"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Peng, L., Chao, W., Shuangmiao, L., and Baocai, F. (2015, January 18\u201320). Research on crack detection method of airport runway based on twice-threshold segmentation. Proceedings of the 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), Qinhuangdao, China.","DOI":"10.1109\/IMCCC.2015.364"},{"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. Civ. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Muduli, P.R., and Pati, U.C. (2013, January 4\u20136). A novel technique for wall crack detection using image fusion. Proceedings of the 2013 International Conference on Computer Communication and Informatics, Coimbatore, India.","DOI":"10.1109\/ICCCI.2013.6466288"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108698","DOI":"10.1016\/j.measurement.2020.108698","article-title":"RENet: Rectangular convolution pyramid and edge enhancement network for salient object detection of cracks","volume":"170","author":"Wang","year":"2021","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shokri, P., Shahbazi, M., and Nielsen, J. (2022). Semantic Segmentation and 3D Reconstruction of Concrete Cracks. Remote Sens., 14.","DOI":"10.3390\/rs14225793"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhao, M., Shi, P., Xu, X., Xu, X., Liu, W., and Yang, H. (2022). Improving the Accuracy of an R-CNN-Based Crack Identification System Using Different Preprocessing Algorithms. Sensors, 22.","DOI":"10.3390\/s22187089"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, X., Zhao, M., Shi, P., Ren, R., He, X., Wei, X., and Yang, H. (2022). Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN. Sensors, 22.","DOI":"10.3390\/s22031215"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yang, F., Zhang, Y.D., and Zhu, Y.J. (2016, January 25\u201328). Road crack detection using deep convolutional neural network. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533052"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Feng, C., Liu, M.-Y., Kao, C.-C., and Lee, T.-Y. (2017, January 25\u201327). Deep active learning for civil infrastructure defect detection and classification. Proceedings of the Computing in Civil Engineering 2017, Seattle, WA, USA.","DOI":"10.1061\/9780784480823.036"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"129659","DOI":"10.1016\/j.conbuildmat.2022.129659","article-title":"Automatic bridge crack detection using Unmanned aerial vehicle and Faster R-CNN","volume":"362","author":"Li","year":"2023","journal-title":"Constr. Build. Mater."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111281","DOI":"10.1016\/j.measurement.2022.111281","article-title":"GPR-based detection of internal cracks in asphalt pavement: A combination method of DeepAugment data and object detection","volume":"197","author":"Liu","year":"2022","journal-title":"Measurement"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1111\/mice.12622","article-title":"Automated crack detection and segmentation based on two-step convolutional neural network","volume":"35","author":"Liu","year":"2020","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mandal, V., Uong, L., and Adu-Gyamfi, Y. (2018, January 10\u201313). Automated road crack detection using deep convolutional neural networks. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622327"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1111\/mice.12367","article-title":"A fast detection method via region-based fully convolutional neural networks for shield tunnel lining defects","volume":"33","author":"Xue","year":"2018","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1111\/mice.12387","article-title":"Road damage detection and classification using deep neural networks with smartphone images","volume":"33","author":"Maeda","year":"2018","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_22","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. Infrastruct. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Deng, L., Zhang, A., Guo, J., and Liu, Y. (2023). An Integrated Method for Road Crack Segmentation and Surface Feature Quantification under Complex Backgrounds. Remote Sens., 15.","DOI":"10.3390\/rs15061530"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"10099","DOI":"10.1109\/TITS.2023.3267433","article-title":"A Semi-Supervised Learning Approach for Pixel-Level Pavement Anomaly Detection","volume":"24","author":"Ren","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109914","DOI":"10.1016\/j.measurement.2021.109914","article-title":"Pixel-level crack segmentation with encoder-decoder network","volume":"184","author":"Tang","year":"2021","journal-title":"Measurement"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"04019040","DOI":"10.1061\/(ASCE)CP.1943-5487.0000854","article-title":"Robust pixel-level crack detection using deep fully convolutional neural networks","volume":"33","author":"Alipour","year":"2019","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_28","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_29","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":"Autom. Constr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"117367","DOI":"10.1016\/j.conbuildmat.2019.117367","article-title":"Image-based concrete crack detection in tunnels using deep fully convolutional networks","volume":"234","author":"Ren","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yang, F., Huo, J., Cheng, Z., Chen, H., and Shi, Y. (2024). An Improved Mask R-CNN Micro-Crack Detection Model for the Surface of Metal Structural Parts. Sensors, 24.","DOI":"10.3390\/s24010062"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, P., Wang, C., Liu, H., Liang, M., Zheng, W., Wang, H., Zhu, S., Zhong, G., and Liu, S. (2023). Research on Automatic Pavement Crack Recognition Based on the Mask R-CNN Model. Coatings, 13.","DOI":"10.3390\/coatings13020430"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022, January 19\u201320). A convnet for the 2020s. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1109\/TPAMI.2019.2956516","article-title":"Cascade R-CNN: High quality object detection and instance segmentation","volume":"43","author":"Cai","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\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":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huang, Z., Huang, L., Gong, Y., Huang, C., and Wang, X. (2019, January 15\u201320). Mask scoring r-cnn. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00657"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Wu, Y., He, K., and Girshick, R. (2020, January 13\u201319). Pointrend: Image segmentation as rendering. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00982"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fang, Y., Yang, S., Wang, X., Li, Y., Fang, C., Shan, Y., Feng, B., and Liu, W. (2021, January 11\u201317). Instances as queries. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00683"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., and Ouyang, W. (2019, January 16\u201320). Hybrid task cascade for instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00511"},{"key":"ref_42","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y.J. (November, January 27). Yolact: Real-time instance segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Vu, T., Kang, H., and Yoo, C.D. (2021, January 2\u20139). Scnet: Training inference sample consistency for instance segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouve, CA, Canada.","DOI":"10.1609\/aaai.v35i3.16374"},{"key":"ref_44","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"04022452","DOI":"10.1061\/(ASCE)MT.1943-5533.0004605","article-title":"Preprocessing of Crack Recognition: Automatic Crack-Location Method Based on Deep Learning","volume":"35","author":"Ren","year":"2023","journal-title":"J. Mater. Civ. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Palevi\u010dius, P., Pal, M., Landauskas, M., Orinait\u0117, U., Timofejeva, I., and Ragulskis, M. (2022). Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows. Sensors, 22.","DOI":"10.3390\/s22103662"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/446\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:44:27Z","timestamp":1760103867000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/446"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,11]]},"references-count":46,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24020446"],"URL":"https:\/\/doi.org\/10.3390\/s24020446","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,11]]}}}