{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:24:56Z","timestamp":1760059496376,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Liaoning Provincial Department of Education","award":["LJKMZ20222194","42071343"],"award-info":[{"award-number":["LJKMZ20222194","42071343"]}]},{"name":"National Natural Science Foundation of China","award":["LJKMZ20222194","42071343"],"award-info":[{"award-number":["LJKMZ20222194","42071343"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The detection of tunnel cracks plays a vital role in ensuring structural integrity and driving safety. However, tunnel environments present significant challenges for crack detection, such as uneven lighting and shadow occlusion, which can obscure surface features and reduce detection accuracy. To address these challenges, this paper proposes a novel crack detection network named STCYOLO. First, a dynamic snake convolution (DSConv) mechanism is introduced to adaptively adjust the shape and size of convolutional kernels, allowing them to better align with the elongated and irregular geometry of cracks, thereby enhancing performance under challenging lighting conditions. To mitigate the impact of shadow occlusion, a Shadow Occlusion-Aware Attention (SOAA) module is designed to enhance the network\u2019s ability to identify cracks hidden in shadowed regions. Additionally, a tiny crack upsampling (TCU) module is proposed, which reorganizes convolution kernels to more effectively preserve fine-grained spatial details during upsampling, thereby improving the detection of small and subtle cracks. The experimental results demonstrate that, compared to YOLOv8, our proposed method achieves a 2.85% improvement in mAP and a 3.02% increase in the F score on the crack detection dataset.<\/jats:p>","DOI":"10.3390\/info16060507","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T07:38:15Z","timestamp":1750232295000},"page":"507","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["STCYOLO: Subway Tunnel Crack Detection Model with Complex Scenarios"],"prefix":"10.3390","volume":"16","author":[{"given":"Jia","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute of Railway Engineering, Liaoning Railway Vocational and Technical College, Jinzhou 121000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Urban Rail Transit, Liaoning Railway Vocational and Technical College, Jinzhou 121000, China"},{"name":"School of Mapping and Geographical Science, Liaoning Technical University, Fuxin 123000, China"},{"name":"School of Civil Engineering, Liaoning University of Technology, Jinzhou 121000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Song","sequence":"additional","affiliation":[{"name":"School of Mapping and Geographical Science, Liaoning Technical University, Fuxin 123000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mapping and Geographical Science, Liaoning Technical University, Fuxin 123000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8978-3173","authenticated-orcid":false,"given":"Miao","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Liaoning University of Technology, Jinzhou 121000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1177\/0020294019877490","article-title":"Intelligent Crack Extraction Based on Terrestrial Laser Scanning Measurement","volume":"53","author":"Yang","year":"2020","journal-title":"Meas. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/s11771-005-0384-3","article-title":"Nondestructive Testing for Crack of Tunnel Lining Using GPR","volume":"12","author":"Liu","year":"2005","journal-title":"J. Cent. S. Univ. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"20","DOI":"10.3141\/2407-03","article-title":"Use of Ultrasonic Tomography to Detect Structural Impairment in Tunnel Linings: Validation Study and Field Evaluation","volume":"2407","author":"White","year":"2014","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"e1308","DOI":"10.1002\/widm.1308","article-title":"Tunnel Crack Detection Using Coarse-to-fine Region Localization and Edge Detection","volume":"9","author":"Li","year":"2019","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11601","DOI":"10.1007\/s11227-022-04330-9","article-title":"Application of Canny Operator Threshold Adaptive Segmentation Algorithm Combined with Digital Image Processing in Tunnel Face Crevice Extraction","volume":"78","author":"Jiang","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ba, Y., Zuo, J., and Jia, Z. (2020, January 13). Image Filtering Algorithms for Tunnel Lining Surface Cracks Based on Adaptive Median-Gaussian. Proceedings of the ICTE 2019; American Society of Civil Engineers, Chengdu, China.","DOI":"10.1061\/9780784482742.096"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_9","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper With Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.undsp.2022.07.003","article-title":"Automatic Tunnel Lining Crack Detection via Deep Learning with Generative Adversarial Network-Based Data Augmentation","volume":"9","author":"Zhou","year":"2023","journal-title":"Undergr. Space"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101206","DOI":"10.1016\/j.aei.2020.101206","article-title":"Automatic Defect Detection of Metro Tunnel Surfaces Using a Vision-Based Inspection System","volume":"47","author":"Li","year":"2021","journal-title":"Adv. Eng. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"125171","DOI":"10.1109\/ACCESS.2023.3330843","article-title":"Tunnel Lining Multi-Defect Detection Based on an Improved You Only Look Once Version 7 Algorithm","volume":"11","author":"Juan","year":"2023","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dai, Q., Xie, Y., Xu, J., Xia, Y., Sheng, C., Tian, C., and Ou, W. (2022, January 14\u201316). Tunnel Crack Identification Based on Improved YOLOv5. Proceedings of the 2022 7th International Conference on Automation, Control and Robotics Engineering (CACRE), Xi\u2019an, China.","DOI":"10.1109\/CACRE54574.2022.9834211"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Li, G., Ma, B., He, S., Ren, X., and Liu, Q. (2020). Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique. Sensors, 20.","DOI":"10.3390\/s20030717"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"15190","DOI":"10.1109\/TITS.2021.3138428","article-title":"Automatic Tunnel Crack Inspection Using an Efficient Mobile Imaging Module and a Lightweight CNN","volume":"23","author":"Liao","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5014711","DOI":"10.1109\/TIM.2022.3184351","article-title":"Tunnel Crack Detection With Linear Seam Based on Mixed Attention and Multiscale Feature Fusion","volume":"71","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1007\/s11709-023-0965-y","article-title":"Fast Detection Algorithm for Cracks on Tunnel Linings Based on Deep Semantic Segmentation","volume":"17","author":"Zhou","year":"2023","journal-title":"Front. Struct. Civ. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., and Dai, J. (2019, January 15\u201320). Deformable ConvNets V2: More Deformable, Better Results. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00953"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Qi, Y., He, Y., Qi, X., Zhang, Y., and Yang, G. (2023, January 1\u20136). Dynamic Snake Convolution Based on Topological Geometric Constraints for Tubular Structure Segmentation. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-End Object Detection with Transformers. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q. (November, January 27). CenterNet: Keypoint Triplets for Object Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00667"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 14\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, Y., Sun, S., Song, W., Zhang, J., and Teng, Q. (2024). CrackYOLO: Rural Pavement Distress Detection Model with Complex Scenarios. Electronics, 13.","DOI":"10.3390\/electronics13020312"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/6\/507\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:54:07Z","timestamp":1760032447000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/6\/507"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,18]]},"references-count":26,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["info16060507"],"URL":"https:\/\/doi.org\/10.3390\/info16060507","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2025,6,18]]}}}