{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:31:30Z","timestamp":1760711490879,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:00:00Z","timestamp":1749772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wenzhou city \u201cunveiled the list of marshals\u2014global attraction of talent\u201d special program","award":["ZR2022004"],"award-info":[{"award-number":["ZR2022004"]}]},{"name":"Jinghong Tian","award":["ZR2022004"],"award-info":[{"award-number":["ZR2022004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Steel plays a fundamental role in modern smart city development, where its surface structural integrity is decisive for operational safety and long-term sustainability. While deep learning approaches show promise, their effectiveness remains limited by inadequate receptive field adaptability, suboptimal feature fusion strategies, and insufficient sensitivity to small defects. To overcome these limitations, we propose FAD-Net, a deep learning framework specifically designed for surface defect detection in steel materials within urban infrastructure. The network incorporates three key innovations: The RFCAConv module, which leverages dynamic receptive field construction and coordinate attention mechanisms to enhance feature representation for defects with long-range spatial dependencies and low-contrast characteristics. The MSDFConv module, employing multi-scale dilated convolutions with optimized dilation rates to preserve fine details while expanding the receptive field. An Auxiliary Head that introduces hierarchical supervision to improve the detection of small-scale defects. Experiments on the GC10-DET dataset showed that FAD-Net achieved 5.0% higher mAP@0.5 than baseline models. Cross-dataset validation with NEU and RDD2022 further confirmed its robustness. These results demonstrate FAD-Net\u2019s effectiveness for automated infrastructure health monitoring.<\/jats:p>","DOI":"10.3390\/bdcc9060158","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T06:19:28Z","timestamp":1749795568000},"page":"158","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FAD-Net: Automated Framework for Steel Surface Defect Detection in Urban Infrastructure Health Monitoring"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7211-6665","authenticated-orcid":false,"given":"Nian","family":"Wang","sequence":"first","affiliation":[{"name":"School of Engineering, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4108-9328","authenticated-orcid":false,"given":"Liyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9013-6371","authenticated-orcid":false,"given":"Jinghong","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Engineering, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103516","DOI":"10.1016\/j.autcon.2020.103516","article-title":"Review of image-based analysis and applications in construction","volume":"122","author":"Mostafa","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.eng.2018.11.030","article-title":"Advances in computer vision-based civil infrastructure inspection and monitoring","volume":"5","author":"Spencer","year":"2019","journal-title":"Engineering"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"29","DOI":"10.29099\/ijair.v2i1.42","article-title":"Computer vision and image processing: A paper review","volume":"2","author":"Wiley","year":"2018","journal-title":"Int. J. Artif. Intell. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100379","DOI":"10.1016\/j.cosrev.2021.100379","article-title":"A survey on deep learning and its applications","volume":"40","author":"Dong","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.ymssp.2018.03.025","article-title":"Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization","volume":"110","author":"Jia","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102470","DOI":"10.1016\/j.rcim.2022.102470","article-title":"Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing","volume":"80","author":"Li","year":"2023","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/TIM.2019.2963555","article-title":"Automated visual defect detection for flat steel surface: A survey","volume":"69","author":"Luo","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_11","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_12","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_13","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2023, January 17\u201324). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Yeh, I.-H., and Liao, H.-Y.M. (2024, January 15\u201320). YOLOv9: Learning what you want to learn using programmable gradient information. Proceedings of the European Conference on Computer Vision, Milan, Italy.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_16","first-page":"107984","article-title":"YOLOv10: Real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C.M., and Dario, P. (2020). Visual-based defect detection and classification approaches for industrial applications\u2014A survey. Sensors, 20.","DOI":"10.3390\/s20051459"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wen, X., Shan, J., He, Y., and Song, K. (2022). Steel surface defect recognition: A survey. Coatings, 13.","DOI":"10.3390\/coatings13010017"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, J., Guo, Z., Jiao, T., and Wang, M. (2018). Defect detection of aluminum alloy wheels in radiography images using adaptive threshold and morphological reconstruction. Appl. Sci., 8.","DOI":"10.3390\/app8122365"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2867","DOI":"10.1007\/s11771-016-3350-3","article-title":"Improved Sobel algorithm for defect detection of rail surfaces with enhanced efficiency and accuracy","volume":"23","author":"Shi","year":"2016","journal-title":"J. Cent. South Univ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.jvcir.2015.04.005","article-title":"Genetic algorithm and mathematical morphology based binarization method for strip steel defect image with non-uniform illumination","volume":"37","author":"Liu","year":"2016","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.jmsy.2015.09.004","article-title":"Automatic thresholding for defect detection by background histogram mode extents","volume":"37","author":"Aminzadeh","year":"2015","journal-title":"J. Manuf. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yan, K., Dong, Q., Sun, T., Zhang, M., and Zhang, S. (2017, January 27\u201329). Weld defect detection based on completed local ternary patterns. Proceedings of the International Conference on Video and Image Processing, Singapore.","DOI":"10.1145\/3177404.3177456"},{"key":"ref_24","first-page":"257","article-title":"Application of Gabor filter to strip surface defect detection","volume":"31","author":"Cong","year":"2010","journal-title":"J. Northeast. Univ. Nat. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"199","DOI":"10.3724\/SP.J.1004.2010.00438","article-title":"Automatic recognition method of surface defects based on Gabor wavelet and kernel locality preserving projections","volume":"36","author":"Wu","year":"2010","journal-title":"Acta Autom. Sin."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5016408","DOI":"10.1109\/TIM.2021.3112227","article-title":"An insulator in transmission lines recognition and fault detection model based on improved faster RCNN","volume":"70","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"112467","DOI":"10.1016\/j.measurement.2023.112467","article-title":"MSC-DNet: An efficient detector with multi-scale context for defect detection on strip steel surface","volume":"209","author":"Liu","year":"2023","journal-title":"Measurement"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s00371-023-02809-x","article-title":"An adaptive loss weighting multi-task network with attention-guide proposal generation for small size defect inspection","volume":"40","author":"Wu","year":"2024","journal-title":"Vis. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1111\/mice.12409","article-title":"Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network","volume":"34","author":"Zhang","year":"2019","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102967","DOI":"10.1016\/j.autcon.2019.102967","article-title":"A deep learning-based framework for an automated defect detection system for sewer pipes","volume":"109","author":"Yin","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112776","DOI":"10.1016\/j.measurement.2023.112776","article-title":"RDD-YOLO: A modified YOLO for detection of steel surface defects","volume":"214","author":"Zhao","year":"2023","journal-title":"Measurement"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, J., Li, S., Wang, Z., Dong, H., Wang, J., and Tang, S. (2020). Using deep learning to detect defects in manufacturing: A comprehensive survey and current challenges. Materials, 13.","DOI":"10.3390\/ma13245755"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhong, Y., Gao, Y., Scott, M.R., and Huang, W. (2021, January 11\u201317). TOOD: Task-aligned one-stage object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bengio, Y. (2012). Practical recommendations for gradient-based training of deep architectures. Neural Networks: Tricks of the Trade, Springer. [2nd ed.].","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"110714","DOI":"10.1016\/j.patcog.2024.110714","article-title":"YOLO-FaceV2: A scale and occlusion aware face detector","volume":"155","author":"Yu","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_41","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., and Shum, H.-Y. (2022). DINO: DETR with improved denoising anchor boxes for end-to-end object detection. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_43","unstructured":"Lyu, C., Zhang, W., Huang, H., Zhou, Y., Wang, Y., Liu, Y., Zhang, S., and Chen, K. (2022). RTMDet: An empirical study of designing real-time object detectors. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 17\u201321). DETRs beat YOLOs on real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/6\/158\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:51:12Z","timestamp":1760032272000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/6\/158"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,13]]},"references-count":45,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["bdcc9060158"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9060158","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2025,6,13]]}}}