Lightweight bearing defect detection method based on collaborative attention and domain adaptive technology

Abstract With the rapid development of industrial manufacturing, bearing defect detection has become a key link to ensure the safe operation of equipment and improve production efficiency. Aiming at the problems of low accuracy and large parameters of traditional bearing defect detection models, this paper proposes a lightweight bearing defect detection method based on collaborative attention and domain adaptive technology. Firstly, the collaborative attention mechanism uses multiple parallel attention heads to extract the features of different regions in the image respectively. Each attention head can independently extract and weigh the features of specific regions, which can capture the complex features of bearing defects more comprehensively, including shape, texture, edge and other key information. At the same time, the domain adaptation technology aims to make the model adapt to the data distribution of different domains. It reduces the difference between the source domain and the object domain by aligning the feature distribution between the source domain and the object domain during the training process, thereby improving the performance of the model on the object domain. The experimental results show that the proposed method is 2.75% higher than the [email protected] of YOLOv7, and has higher computational efficiency and better generalization ability than the traditional method.