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We propose YOLO\u2011MSD, a lightweight surface defect detection model that integrates two key designs: (1) a novel four-scale backbone that effectively extracts small and multi-scale targets from large-size images by enhancing feature representation across different scale resolutions, and (2) a streamlined feature\u2011pyramid neck that boosts cross\u2011scale fusion while reducing parameters and computational cost. Extensive experiments on five public datasets verify the model\u2019s effectiveness. On the PCB, HRIPCB and GC10\u2011DET datasets featuring high-resolution images, YOLO\u2011MSD achieves 96.67% <jats:italic>mAP<\/jats:italic>, 96.62% <jats:italic>mAP<\/jats:italic> and 69.09% <jats:italic>mAP<\/jats:italic>, respectively, while maintaining a low parameter count and computational complexity. It also outperforms most advanced models on two additional public datasets and achieves 20.82 FPS with a power consumption of 6.95 W on the PCB dataset when deployed on a Jetson Xavier NX edge device. These results demonstrate the accuracy, efficiency, and deployability of YOLO\u2011MSD for industrial surface\u2011defect detection.<\/jats:p>","DOI":"10.1007\/s10489-025-06739-0","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T05:02:59Z","timestamp":1751518979000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["YOLO-MSD: a robust industrial surface defect detection model via multi-scale feature fusion"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3479-9802","authenticated-orcid":false,"given":"Yifei","family":"Ge","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4554-6018","authenticated-orcid":false,"given":"Zhuo","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4351-6923","authenticated-orcid":false,"given":"Lin","family":"Meng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"6739_CR1","doi-asserted-by":"publisher","unstructured":"Guo J, Liu P, Xiao B, Deng L, Wang Q (2024) Surface defect detection of civil structures using images: Review from data perspective. 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