{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T12:07:03Z","timestamp":1784117223175,"version":"3.55.0"},"reference-count":22,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T00:00:00Z","timestamp":1720915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific and Technological Developing Project of Jilin Province","award":["20240402076GH"],"award-info":[{"award-number":["20240402076GH"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Detection of pavement diseases is crucial for road maintenance. Traditional methods are costly, time-consuming, and less accurate. This paper introduces an enhanced pavement disease recognition algorithm, MS-YOLOv8, which modifies the YOLOv8 model by incorporating three novel mechanisms to improve detection accuracy and adaptability to varied pavement conditions. The Deformable Large Kernel Attention (DLKA) mechanism adjusts convolution kernels dynamically, adapting to multi-scale targets. The Large Separable Kernel Attention (LSKA) enhances the SPPF feature extractor, boosting multi-scale feature extraction capabilities. Additionally, Multi-Scale Dilated Attention in the network\u2019s neck performs Spatially Weighted Dilated Convolution (SWDA) across different dilatation rates, enhancing background distinction and detection precision. Experimental results show that MS-YOLOv8 increases background classification accuracy by 6%, overall precision by 1.9%, and mAP by 1.4%, with specific disease detection mAP up by 2.9%. Our model maintains comparable detection speeds. This method offers a significant reference for automatic road defect detection.<\/jats:p>","DOI":"10.3390\/s24144569","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T14:15:49Z","timestamp":1721052949000},"page":"4569","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["MS-YOLOv8-Based Object Detection Method for Pavement Diseases"],"prefix":"10.3390","volume":"24","author":[{"given":"Zhibin","family":"Han","sequence":"first","affiliation":[{"name":"School of Transportation, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yutong","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Transportation, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anqi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Transportation, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiran","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Transportation, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9098-2666","authenticated-orcid":false,"given":"Ciyun","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Transportation, Jilin University, Changchun 130022, China"},{"name":"Jilin Engineering Research Center for Intelligent Transportation System, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104436","DOI":"10.1016\/j.autcon.2022.104436","article-title":"Multi-scale feature fusion network for pixel-level pavement distress detection","volume":"141","author":"Zhong","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_2","unstructured":"Zheng, J., and Ren, J. (2023). Road Disease Detection based on Latent Domain Background Feature Separation and Suppression. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cui, L., Qi, Z., Chen, Z., and Meng, F. (2015, January 8\u20139). Pavement distress detection using random decision forests. Proceedings of the Data Science: Second International Conference, ICDS 2015, Sydney, Australia.","DOI":"10.1007\/978-3-319-24474-7_14"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"012048","DOI":"10.1088\/1742-6596\/1821\/1\/012048","article-title":"Classification of damaged road types using multiclass support vector machine (SVM)","volume":"1821","author":"Sulistyaningrum","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_5","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. 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