AUTHOR=Wang Luyang , Zhang Gongxue , Wang Weijun , Chen Jinyuan , Jiang Xuyao , Yuan Hai , Huang Zucheng TITLE=A defect detection method for industrial aluminum sheet surface based on improved YOLOv8 algorithm JOURNAL=Frontiers in Physics VOLUME=12 YEAR=2024 URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2024.1419998 DOI=10.3389/fphy.2024.1419998 ISSN=2296-424X ABSTRACT=

In industrial aluminum sheet surface defect detection, false detection, missed detection, and low efficiency are prevalent challenges. Therefore, this paper introduces an improved YOLOv8 algorithm to address these issues. Specifically, the C2f-DSConv module incorporated enhances the network’s feature extraction capabilities, and a small target detection layer (160 × 160) improves the recognition of small targets. Besides, the DyHead dynamic detection head augments target representation, and MPDIoU replaces the regression loss function to refine detection accuracy. The improved algorithm is named YOLOv8n-DSDM, with experimental evaluations on an industrial aluminum sheet surface defect dataset demonstrating its effectiveness. YOLOv8n-DSDM achieves an average mean average precision (mAP50%) of 94.7%, demonstrating a 3.5% improvement over the original YOLOv8n. With a single-frame detection time of 2.5 ms and a parameter count of 3.77 M, YOLOv8n-DSDM meets the real-time detection requirements for industrial applications.