YOLOv5-CD: Strip steel surface defect detection method based on coordinate attention and a decoupled head

Abstract

The ability to detect strip steel surface defects is crucial for ensuring the quality of items made from that material. A YOLOv5-CD algorithm is proposed to overcome the problems of insufficient feature extraction ability, low detection accuracy, and slow convergence rate of the strip steel surface defect detection technique in an industrial scenario. First, a coordination attention mechanism is added to the backbone network. This strategy embeds positional information into channel attention, addressing the issue of positional information loss caused by global pooling and effectively improving the model's feature extraction capacity. Second, the head component uses a decoupled head detector. By separating the classification and regression tasks, this approach effectively increases the model's detection precision and convergence rate. According to the experimental findings, the mAP of the YOLOv5-CD algorithm on the NEU-DET dataset is 80.6 %, 5.8 % higher than that of the original YOLOv5 method. Additionally, the algorithm's convergence speed is faster and better able to meet the demands of actual industrial production.