Abstract
This paper addresses the industrial demand for precision and efficiency in metal surface defect detection by proposing SLF-YOLO, a lightweight object detection model designed for resource-constrained environments. The key innovations of SLF-YOLO include a novel SC_C2f module with a channel gating mechanism to enhance feature representation and regulate information flow, and a newly designed Light-SSF_Neck structure to improve multi-scale feature fusion and morphological feature extraction. Additionally, an improved FIMetal-IoU loss function is introduced to boost generalization performance, particularly for fine-grained and small-target defects. Experimental results demonstrate that SLF-YOLO achieves a mean Average Precision (mAP) of 80.0% on the NEU-DET dataset, outperforming YOLOv8's 75.9%. On the AL10-DET dataset, SLF-YOLO achieves a mAP of 86.8%, striking an effective balance between detection accuracy and computational efficiency without increasing model complexity. Compared to other mainstream models, SLF-YOLO demonstrates strong detection accuracy while maintaining a lightweight architecture, making it highly suitable for industrial applications in metal surface defect detection. The source code is available at https://github.com/zacianfans/SLF-YOLO .