EDGL-Net: An Efficient Dynamic Global–Local Network for Real-Time Metal Surface Defect Detection in Industrial Edge Intelligence

Metal component manufacturing requires stringent surface quality standards to prevent structural failures in critical applications. Metal surface defect detection remains challenging in resource-constrained industrial edge environments. Highly textured, non-stationary backgrounds easily obscure tiny defects with weak visual saliency. Furthermore, such defects exhibit pronounced anisotropic geometry. Existing models struggle to achieve global semantic understanding, accurate geometric alignment, and real-time inference under limited computational budgets. These limitations lead to frequent detection failures. EDGL-Net is proposed as an adaptive multi-scale detection architecture for edge deployment. EDGL-Net integrates global context modeling and anisotropic geometric feature extraction. It incorporates a parameter-sharing multi-scale prediction mechanism to enhance robustness for small and elongated defects. Experiments on the NEU-DET and GC10-DET datasets show that the proposed method achieves a favorable balance between detection accuracy and computational efficiency. EDGL-Net improves mAP@0.5 by 2.7 points and Precision by 6.0 points over the baseline on NEU-DET. It consumes 64% of the computational resources required by mainstream models.