Research on improving the detection method of welding defects in drum welding of CoT-YOLOv7

In response to the problem of low efficiency and accuracy in detecting welding defects on the surface of coal mining machine drums, a drum welding defect automatic detection method based on the improved YOLOv7 algorithm is proposed. By utilizing the characteristic of long-distance fusion of multiple feature information in the Transformer model, the global context CoT model is introduced into the YOLOv7 algorithm network, and experimental verification is conducted. The results show that this detection method has high detection accuracy in welding defect detection and can eliminate the image redundancy problem caused by the introduction of attention mechanism. It can be applied to the quality inspection of coal mining machine drum welding products.