Part Defect Detection Method Based on Channel-Aware Aggregation and Re-Parameterization Asymptotic Module

Abstract

In industrial production, the quality, reliability, and precision of parts determine the overall quality and performance of various mechanical equipment. However, existing part defect detection methods have shortcomings in terms of feature extraction and fusion, leading to issues of missed detection. To address this challenge, this manuscript proposes a defect detection algorithm for parts (CRD-YOLO) based on the improved YOLOv5. Our first aim is to increase the regional features of small targets and improve detection accuracy. In this manuscript, we design the channel- aware aggregation (CAA) module, utilizing a multi-branch convolutional segmentation structure and incorporating an attention mechanism and ConvNeXt V2 Block as bottleneck layers for feature processing. Secondly, the re-parameterization asymptotic module (RAFPN) is used to replace the original model neck structure in order to improve the interaction between shallow-detail features and deeper semantic features, and to avoid the large semantic gaps between non-neighboring layers. Then, the DO-DConv module is encapsulated within the BN layer and the LeakyReLU activation function to become the DBL module, which further processes the feature output from the backbone network and fuses neck features more comprehensively. Finally, experiments with the self-made dataset show that the model proposed in this paper improves the accuracy of detecting various types of defect. In particular, it increased the accuracy of detecting bearing scuffing defects with significant dimensional variations, with an improvement of 6%, and gear missing teeth defects with large shape differences, with an 8.3% enhancement. Additionally, the mean average precision (mAP) reached 96.7%, an increase of 5.5% and 6.4% compared to YOLOv5s and YOLOv8s, respectively.