Ceramic ring defect detection based on improved YOLOv5

Abstract

For the problem that ceramic ring defects are small and difficult to detect with many types; and the defect feature information is weak and difficult to extract, this paper proposes an improved YOLOv5-based target detection method to achieve the detection of ceramic ring defects. By adding an attention mechanism to the Backbone part of YOLOv5, the attention of the network model to different types of defects can be improved, the interference of irrelevant background can be reduced, and the network can extract the channel features and spatial features of the defects more effectively, which can effectively enhance the detection capability of the model. The experimental results show that the ceramic ring defect detection method proposed in this paper can accurately detect defects with an mAP value of 89.9%, which is 1.1% better compared with the original YOLOv5 algorithm. It provides an effective detection method for defect detection of ceramic ring parts.