Stainless steel welded pipe weld seam defect detection method based on improved YOLOv5s

Aiming at the problem of low efficiency and low accuracy of stainless steel welded pipe weld surface defect detection in industrial scenarios, a welded pipe defect detection algorithm based on the improved YOLOv5s is proposed. First, the C3 module in YOLOv5s is replaced with the C2f module, which has fewer parameters and is more sensitive to small targets, to obtain richer gradient information and enhance the feature extraction capability. Secondly, the SimAM parameter-free attention mechanism is introduced to strengthen the model's simultaneous attention to channel and spatial information, which gives higher priority to defective features without increasing any number of parameters, improves the accuracy of the model, and improves the detection performance of the model in various scenarios. The experimental findings demonstrate that the improved algorithm, compared with the traditional YOLOv5s algorithm, improves the mAP by 4.4%,the frame rate FPS reaches 140F/s, and the number of parameters is reduced by 18%; it is easier to be deployed on the inspection equipment in the production environment while meeting the need for rapid and precise detection. The approach offers robust support for detecting defects in stainless steel welded pipe, and provides a reference for inspection intelligence in on-line production of welded pipe.