Detection of arc shape in ultra-narrow gap welding based on improved YOLOv5s

In ultra-narrow gap welding, it is necessary to detect the aspect ratio parameters of arc shape in real time and efficiently. However, the existing arc shape method can not realize on-line detection. To solve this problem, this paper proposes a lightweight arc detection network AD-YOLOV5 based on YOLOv5s network model. To reduce the complexity of YOLOv5s network, the Repvgg Block module is used to replace the CONV module in Backbone network, and the coordinate attention mechanism is introduced in Neck network to guarantee the lightness of YOLOv5s network and improve the precision of model. The experimental results show that the model size is reduced by 65% and the detection speed is increased by 50% while the detection accuracy of aspect ratio remains unchanged. The implementation of the method in this paper provides a reference for the online monitoring of ultranarrow gap welding quality.