RFG ‐ YOLO : Accurate and Lightweight Detection of Multi‐Scale Defects on Metallic Surfaces

ABSTRACT Surface defect detection is critical in industrial applications, especially for multi‐scale small object detection under complex backgrounds, low resolution, and real‐time constraints. Traditional YOLO‐based detectors face three key limitations: single‐path backbones struggle to balance fine‐grained details and high‐level semantics simultaneously, conventional feature pyramids cause small‐object feature dilution through simple additive fusion, and detection heads lack explicit quality estimation for diverse defect morphologies. To address these challenges, we propose RFG‐YOLO, a lightweight detection network. The proposed method introduces three key innovations: DPFNet employs dual‐path parallel processing with PSHGNetv2 and YOLOv11 backbone to extract fine‐grained details and semantic context simultaneously, eliminating the single‐path bottleneck; MSWFPN utilizes weighted fusion modules including MFM, SCFM, and CSP‐SMSFB to prevent small‐object feature dilution through adaptive multi‐scale integration; RQCD incorporates location quality estimation with RepConv to refine bounding box predictions for diverse defect shapes, improving localization precision across scales. Experiments on NEU‐DET, GEAR‐DET, and GC10‐DET demonstrate that RFG‐YOLO achieves mAP@50 improvements of 4.1%, 5.77%, and 4.1%, respectively, while reducing model parameters by 60.5% and computational cost by 50.8%. The model achieves a runtime speed of 156.0 frames per second on a GPU. These results validate that RFG‐YOLO strikes an optimal balance between detection accuracy and computational efficiency, rendering it highly suitable for real‐time defect detection in industrial settings.