Abstract: Currently,many researchers use deep learning for surface defect detection.However,most of these studies follow the mainstream object detection algorithm and focus on high-level semantic features while neglecting the importance of low-level semantic information(color,shape) for surface defect detection,resulting in unsatisfactory defect detection effect.To address this issue,a metal surface defect detection network called the dual-stream YOLOv4 network is proposed.The backbone network is split into two branches,with inputs consisting of high-resolution and low-resolution images.The shallow branch is responsible for extracting low-level features from the high-resolution image,while the deep branch is responsible for extracting high-level features from the low-resolution image.The model's total parameter volume is reduced by cutting down the number of layers and channels in both branches.To enhance the low-level semantic features,a tree-structured multi-scale feature fusion method(TMFF) is proposed,and a feature fusion module with a polarized self-attention mechanism and spatial pyramid pooling(FFM-PSASPP) is designed and applied to the TMFF.The algorithm's map@50 results on the test sets of the Northeastern University hot-rolled strip surface defect dataset(NEU-DET),the metal surface defect dataset(GC10-DET),and the enaiter rice cooker inner pot defect dataset are 0.80,0.66,and 0.57,respectively.Compared to most mainstream object detection algorithms used for defect detection,there is an improvement,and the model's parameter volume is only half that of the original YOLOv4,with a speed close to YOLOv4,making it suitable for practical use.

Key words: Metal surface defect detection, Object detection, YOLOv4, Dual-stream backbone network, Multi-scale feature enhancement