ESD-YOLOv5: A Full-Surface Defect Detection Network for Bearing Collars

Abstract

To address the different forms and sizes of bearing collar surface defects, uneven distribution of defect positions, and complex backgrounds, we propose ESD-YOLOv5, an improved algorithm for bearing collar full-surface defect detection. First, a hybrid attention module, ECCA, was constructed by combining an efficient channel attention (ECA) mechanism and a coordinate attention (CA) mechanism, which was introduced into the YOLOv5 backbone network to enhance the localization ability of object features by the network. Second, the original neck was replaced by the constructed Slim-neck, which reduces the model’s parameters and computational complexity without sacrificing accuracy for object detection. Furthermore, the original head was replaced by the decoupled head from YOLOX, which separates the classification and regression tasks for object detection. Last, we constructed a dataset of defective bearing collars using images collected from industrial sites and conducted extensive experiments. The results demonstrate that our proposed ESD-YOLOv5 detection model achieved an mAP of 98.6% on our self-built dataset, which is a 2.3% improvement over the YOLOv5 base model. Moreover, it outperformed mainstream one-stage object detection algorithms. Additionally, the bearing collar surface defect detection system developed based on our proposed method has been successfully applied in the industrial domain for bearing collar inspection.