Metal Surface Defect Detection Method Based on Dual-stream YOLOv4
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 se-mantic 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 fea-tures 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(TM-FF)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 de-fect 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.
Metal surface defect detectionObject detectionYOLOv4Dual-stream backbone networkMulti-scale feature en-hancement
NETL
NSTL
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