{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T18:49:32Z","timestamp":1768762172282,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Natural Science Foundation","award":["L242153"],"award-info":[{"award-number":["L242153"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Road defect detection is essential for timely road damage repair and traffic safety assurance. However, existing object detection algorithms suffer from insufficient accuracy in detecting small road surface defects and are prone to missed detections and false alarms under complex lighting and background conditions. To address these challenges, this study proposes SDC-YOLOv8, an improved YOLOv8-based algorithm for road defect detection that employs attention-enhanced feature learning and adaptive feature reconstruction. The model incorporates three key innovations: (1) an SPPF-LSKA module that integrates Fast Spatial Pyramid Pooling with Large Separable Kernel Attention to enhance multi-scale feature representation and irregular defect modeling capabilities; (2) DySample dynamic upsampling that replaces conventional interpolation methods for adaptive feature reconstruction with reduced computational cost; and (3) a Coordinate Attention module strategically inserted to improve spatial localization accuracy under complex conditions. Comprehensive experiments on a public pothole dataset demonstrate that SDC-YOLOv8 achieves 78.0% mAP@0.5, 81.0% Precision, and 70.7% Recall while maintaining real-time performance at 85 FPS. Compared to the baseline YOLOv8n model, the proposed method improves mAP@0.5 by 2.0 percentage points, Precision by 3.3 percentage points, and Recall by 1.8 percentage points, yielding an F1 score of 75.5%. These results demonstrate that SDC-YOLOv8 effectively enhances small-target detection accuracy while preserving real-time processing capability, offering a practical and efficient solution for intelligent road defect detection applications.<\/jats:p>","DOI":"10.3390\/s26020609","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T11:39:37Z","timestamp":1768563577000},"page":"609","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SDC-YOLOv8: An Improved Algorithm for Road Defect Detection Through Attention-Enhanced Feature Learning and Adaptive Feature Reconstruction"],"prefix":"10.3390","volume":"26","author":[{"given":"Hao","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulong","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1479-1515","authenticated-orcid":false,"given":"Enhao","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danyang","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10581","DOI":"10.1109\/TITS.2024.3382837","article-title":"Road surface defect detection\u2014From image-based to non-image-based: A survey","volume":"25","author":"Yu","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sun, Z., Zhu, L., Qin, S., Yu, Y., Ju, R., and Li, Q. (2024). Road surface defect detection algorithm based on YOLOv8. Electronics, 13.","DOI":"10.3390\/electronics13122413"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.3934\/era.2024078","article-title":"Research and optimization of YOLO-based method for automatic pavement defect detection","volume":"32","author":"Yao","year":"2024","journal-title":"Electron. Res. Arch."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Dong, D., and Li, Z. (2021). Smartphone sensing of road surface condition and defect detection. Sensors, 21.","DOI":"10.3390\/s21165433"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14531","DOI":"10.1109\/ACCESS.2020.2966881","article-title":"Review of pavement defect detection methods","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s11760-024-03770-5","article-title":"RIEC-YOLO: An improved road defect detection model based on YOLOv8","volume":"19","author":"Liu","year":"2025","journal-title":"Signal Image Video Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s00607-020-00869-8","article-title":"An improved YOLO-based road traffic monitoring system","volume":"103","author":"Abbasi","year":"2021","journal-title":"Computing"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bao, H., Wu, W., Yu, J., Cao, Y., Liu, Y., Zhang, H., Li, X., and Shi, W. (2024, January 24\u201326). Application of YOLO model in road defect detection. Proceedings of the 2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Nanjing, China.","DOI":"10.1109\/AINIT61980.2024.10581746"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1080\/10298436.2020.1714047","article-title":"Pavement distress detection and classification based on YOLO network","volume":"22","author":"Du","year":"2021","journal-title":"Int. J. Pavement Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hussain, M. (2023). YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection. Machines, 11.","DOI":"10.3390\/machines11070677"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"114602","DOI":"10.1016\/j.eswa.2021.114602","article-title":"A survey and performance evaluation of deep learning methods for small object detection","volume":"172","author":"Liu","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Krishna, H., and Jawahar, C.V. (2017, January 26\u201329). Improving small object detection. Proceedings of the 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), Nanjing, China.","DOI":"10.1109\/ACPR.2017.149"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106838","DOI":"10.1109\/ACCESS.2019.2932731","article-title":"An improved faster R-CNN for small object detection","volume":"7","author":"Cao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mirzaei, B., Nezamabadi-Pour, H., Raoof, A., and Derakhshani, R. (2023). Small object detection and tracking: A comprehensive review. Sensors, 23.","DOI":"10.3390\/s23156887"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6283","DOI":"10.1007\/s00521-024-09422-6","article-title":"A review of small object detection based on deep learning","volume":"36","author":"Wei","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Sami, A.A., Sakib, S., Deb, K., and Sarker, I.H. (2023). Improved YOLOv5-Based Real-Time Road Pavement Damage Detection in Road Infrastructure Management. Algorithms, 16.","DOI":"10.3390\/a16090452"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s13634-022-00931-x","article-title":"YOLO-LRDD: A Lightweight Method for Road Damage Detection Based on Improved YOLOv5s","volume":"2022","author":"Wan","year":"2022","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"12052","DOI":"10.1038\/s41598-024-62933-z","article-title":"YOLOv8-PD: An Improved Road Damage Detection Algorithm Based on YOLOv8n Model","volume":"14","author":"Zeng","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e2593","DOI":"10.7717\/peerj-cs.2593","article-title":"OBC-YOLOv8: An Improved Road Damage Detection Model Based on YOLOv8","volume":"11","author":"Zhang","year":"2025","journal-title":"PeerJ Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.procs.2025.04.240","article-title":"Research on Crack Segmentation and Detection of Red Brick Wall Structure Based on Deep Learning","volume":"261","author":"Peng","year":"2025","journal-title":"Procedia Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"26877","DOI":"10.1109\/JSEN.2024.3418618","article-title":"Automated Defect Detection for Mass-Produced Electronic Components Based on YOLO Object Detection Models","volume":"24","author":"Mao","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Yeh, I.-H., and Liao, H.-Y.M. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. arXiv.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_25","unstructured":"DataCluster Labs (2023). Potholes or Cracks on Road Image Dataset, DataCluster Labs."},{"key":"ref_26","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2023, February 28). YOLO by Ultralytics. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107062","DOI":"10.1016\/j.compag.2022.107062","article-title":"Complete and accurate holly fruits counting using YOLOX object detection","volume":"198","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lin, T., 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 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"121352","DOI":"10.1016\/j.eswa.2023.121352","article-title":"Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNN","volume":"236","author":"Lau","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, W., Lu, H., Fu, H., and Cao, Z. (2023, January 1\u20136). Learning to Upsample by Learning to Sample. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00554"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, K., Xu, R., Liu, Z., Loy, C.C., and Lin, D. (November, January 27). CARAFE: Content-Aware ReAssembly of Features. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00310"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lu, H., Liu, W., Fu, H., and Cao, Z. (2022). FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling. Computer Vision\u2013ECCV 2022, Springer.","DOI":"10.1007\/978-3-031-19812-0_14"},{"key":"ref_33","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_34","unstructured":"Fu, S., Hamilton, M., Brandt, L., Feldman, A., Zhang, Z., and Freeman, W.T. (2024). Featup: A Model-Agnostic Framework for Features at Any Resolution. arXiv."},{"key":"ref_35","unstructured":"Jocher, G., and Chaurasia, A. (2020, January 01). Ultralytics YOLOv5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huang, Y.-Q., Zheng, J.-C., Sun, S.-D., Yang, C.-F., and Liu, J. (2020). Optimized YOLOv3 Algorithm and Its Application in Traffic Flow Detections. Appl. Sci., 10.","DOI":"10.3390\/app10093079"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Norkobil Saydirasulovich, S., Abdusalomov, A., Jamil, M.K., Nasimov, R., Kozhamzharova, D., and Cho, Y.-I. (2023). A YOLOv6-Based Improved e Detection Approach for Smart City Environments. Sensors, 23.","DOI":"10.3390\/s23063161"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/2\/609\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T05:23:33Z","timestamp":1768713813000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/2\/609"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,16]]},"references-count":37,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["s26020609"],"URL":"https:\/\/doi.org\/10.3390\/s26020609","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,16]]}}}