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<p>Steel surface defect detection constitutes a critical inspection task in industrial production. To address challenges including missed detections and low accuracy for fine defects, this study develops an enhanced Faster R-CNN algorithm. The proposed framework incorporates a feature fusion module and lightweight channel attention mechanism between Feature Pyramid Networks (FPN) and Region Proposal Network (RPN), substantially augmenting subtle feature extraction capabilities. Evaluated on the NEU-DET dataset, the optimized model achieves a mean average precision (mAP) of 80.2%-yielding a 12.6% improvement over the baseline-while increasing detection speed by 40.9%. This approach not only significantly elevates defect recognition accuracy but also establishes a practical framework for automated steel surface inspection systems.</p>

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Improved faster R-CNN for steel surface defect detection in industrial quality control

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月17日

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  • Yuefeng Leng,
  • Jiazhi Liu

摘要

Steel surface defect detection constitutes a critical inspection task in industrial production. To address challenges including missed detections and low accuracy for fine defects, this study develops an enhanced Faster R-CNN algorithm. The proposed framework incorporates a feature fusion module and lightweight channel attention mechanism between Feature Pyramid Networks (FPN) and Region Proposal Network (RPN), substantially augmenting subtle feature extraction capabilities. Evaluated on the NEU-DET dataset, the optimized model achieves a mean average precision (mAP) of 80.2%-yielding a 12.6% improvement over the baseline-while increasing detection speed by 40.9%. This approach not only significantly elevates defect recognition accuracy but also establishes a practical framework for automated steel surface inspection systems.