关键词: YOLOv8, 金属表面缺陷检测, 特征提取, 多尺度, 注意力机制

Abstract: To reduce the problems of false positives and missed detections in metal surface defect detection task and enhance the model’s detection capability for multi-scale and similar defects, the DFC-YOLO detection approach is proposed. Firstly, the diversity feature extraction module (DFEM) is designed to extract more comprehensive features from the multi-scale information processed by SPPF, improving the detection accuracy of targets at different scales. Secondly, the feature processing module (FPM) is designed to integrate shallow and deep feature information, while the shallow feature extraction module (SFEM) is used to fully extract key information from the shallow layers, reducing interference from background noise and improving the model’s ability to recognize similar targets. Finally, in order to further enhance the model’s ability to extract and integrate multi-scale features without significantly increasing memory and computational costs, the C2f_RFEM module is proposed. This module uses the receptive field expansion module (RFEM) to enlarge the model’s receptive field, obtain more contextual information, and improve detection performance. The experimental results show that DFC-YOLO achieves a mAP of 77.60% on the GC10-DET dataset, which is 3.60 percentage points higher than the original network. On the NEU-DET dataset, the mAP increases by 2.55 percentage points. The experimental results validate the feasibility and generalization of the proposed method and demonstrate its effective application to metal surface defect detection.

Key words: YOLOv8, metal surface defect detection, feature extraction, multi-scale, attention mechanis