STUDY ON SURFACE DEFECT DETECTION METHOD OF YOLOV4-TINY STRIP BY MULTI-FEATURE FUSION

Automatic identification of small surface defects is one of the difficulties in strip production. In order to improve the accuracy of surface defect detection of strip steel, a multi-feature fusion YOLOv4-tiny deep learning method is proposed. The Inception structure and multi-scale information were introduced. The orientation gradient histogram feature (HOG) of the original image was extracted and fused with the high-level features extracted from the backbone network as the input of the feature pyramid structure. The experimental results show that the mAP of surface defects of strip steel in the test concentration is 93.99%, which is 13.57 percentage points higher than that of the YOLOv4-tiny network. The number of network parameters was reduced by about 210 000 compared with that of the YOLOv4-tiny network, and the network detection accuracy is greatly improved.