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<p>Surface defects on strip steel are often inevitable due to limitations in raw materials and manufacturing processes. To improve defect detection accuracy, we propose the YOLOv5s-SDF algorithm, which integrates ShuffleAttention in the neck and DyHead in the head of the YOLOv5s framework. The novelty lies in the use of ShuffleAttention to model spatial-channel dependencies and the incorporation of Focal-CIoU loss to mitigate the influence of low-quality samples. Experiments on the NEU-DET dataset show that YOLOv5s-SDF achieves 69.37% <i>Precision</i>, 74.21% <i>Recall</i>, 76.32% <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(mAP_{0.5}\)</EquationSource> </InlineEquation>, and 38.60% <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(mAP_{0.5-0.95}\)</EquationSource> </InlineEquation>, outperforming the baseline YOLOv5s by 0.2%, 1.72%, 3.57%, and 0.84%, respectively.</p>

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Attention-guided YOLOv5s-SDF model for accurate detection of strip steel surface defects

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月26日

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  • Kun Liu,
  • Xinbo Chang,
  • Hongru Ma,
  • Liang Lv,
  • Hang Chen

摘要

Surface defects on strip steel are often inevitable due to limitations in raw materials and manufacturing processes. To improve defect detection accuracy, we propose the YOLOv5s-SDF algorithm, which integrates ShuffleAttention in the neck and DyHead in the head of the YOLOv5s framework. The novelty lies in the use of ShuffleAttention to model spatial-channel dependencies and the incorporation of Focal-CIoU loss to mitigate the influence of low-quality samples. Experiments on the NEU-DET dataset show that YOLOv5s-SDF achieves 69.37% Precision, 74.21% Recall, 76.32% \(mAP_{0.5}\) , and 38.60% \(mAP_{0.5-0.95}\) , outperforming the baseline YOLOv5s by 0.2%, 1.72%, 3.57%, and 0.84%, respectively.