To address the issues of low accuracy, high false positives, and missed detections in hot-rolled strip steel surface defect inspection, this paper proposes an improved detection model named DFEM-NET based on YOLOv8n. First, an efficient feature extraction module (DSC2f) based on Dynamic Snake Convolution is designed to enhance the model’s capability in capturing features of irregular and elongated defects. Second, a Feature Pyramid Shared Convolution module (FPSC) is constructed to expand the model’s receptive field and effectively suppress interference from complex backgrounds. Third, an Enhanced Feature Correction (EFC) strategy is adopted during the feature fusion stage to help the model better learn the detailed features of small defect targets. Finally, a Multi-Scale Attention Aggregation module (MSAA) is introduced before the detection head, enabling the network to focus on critical feature information and thereby comprehensively improve detection accuracy for target defects. Experimental results demonstrate that, compared to the baseline model YOLOv8n, DFEM-NET achieves a detection accuracy (mAP@0.5) of 83.5%, representing an increase of 4.8%; a recall rate of 76.4%, an increase of 3.3%; and a precision of 84.7%, an increase of 3.1%, without a significant increase in model complexity. Furthermore, generalization experiments conducted on the GC10-DET dataset confirm that the proposed algorithm exhibits exceptional generalization capability.
Shao et al. (Thu,) studied this question.