Research on rapid detection of cross-scale defects in surface based on deep learning

The complex and diverse forms of surface defects in metal cutting, as well as their large scale span, present new challenges for deep learning algorithms. In addition, the existing defect detection models are generally characterized by high computational amount and complex structures, which is contrary to the high real-time performance and limited computing resources required in industrial applications. Based on this, this paper proposes a rapid detection method for cross-scale defects in surfaces based on deep learning. Firstly, a dataset of defect surfaces is collected and constructed through cutting experiments. Then, the experimental analysis reveals the insufficiency of the You Only Look Once version-5 s (YOLOv5s) network model for the detection of cross-scale defects on the surface. As a result, a RepVGG-Coordinate Attention-YOLOv5s (Rep-CA-YOLOv5s) network model, suitable for cross-scale defect detection, is proposed. This model optimizes the YOLOv5s network model from three perspectives, enhancing its ability to extract and fuse features for cross-scale defects. Finally, this paper investigates methods to improve detection speed while ensuring model accuracy. Two optimal Rep-CAYOLOv5s sparse models are obtained through sparse training based on the gamma scaling factor of the Batch Normalization (BN) layer and the filter weight, respectively. The relationship between detection accuracy, parameter quantity, and inference speed of these two sparse models under different pruning rates is explored. Experimental results indicate that the filter pruning method significantly improves model inference speed. At a 50 % pruning rate, optimal detection results can be achieved. Compared with the unpruning model, the pruned model reduced the inference speed by 55.67 %, while the mean Average Precision (mAP) only decreased by 0.1 %.