Welding defect detection based on YOLOv8

Abstract In this paper, we propose an improvement scheme for the unclear boundary capture issues of the YOLOv8 algorithm in weld defect detection. We replace the Conv module with the CG black module in Backbone to enhance the algorithm’s ability to extract image features so as to capture weld defects more accurately. Meanwhile, the CBAM attention mechanism is introduced in the tail of the Backbone to improve the model’s capability to learn the correlation of different regions of the image and improve the generalization performance. The experimental findings substantiate that the enhanced algorithm exhibits superior accuracy in comparison to the original algorithm, recall rate, Map@0.5, and other indicators of the improved algorithm. The enhanced algorithm demonstrates a 3.6% increase in accuracy when contrasted with the original algorithm.