Research on robot riveting defect detection method based on improved DETR

Riveting is the main connection method for structural components of railway vehicles, and qualified riveting quality is an important guarantee for the safe and stable operation of vehicles. Aiming at the problems of low detection accuracy, few detection points, and low level of intelligent detection in existing riveting defect detection methods, a robot riveting defect detection method based on improved DETR was proposed. First, a riveting defect detection system was established, which sequentially collected riveting defect images under working conditions of large workpiece size and small rivet size. Second, in order to enhance the image feature extraction ability and detection performance of the DETR model in small targets, EfficientNet was used as the backbone feature extraction network in DETR. The 3D weighted attention mechanism SimAM was introduced into the EfficientNet network, effectively preserving the header shape information of the image feature layer and the spatial information of the rivet point area. Then, a weighted bidirectional feature pyramid module was introduced into the neck network. The output of the EfficientNet network was used as the input of the feature fusion module to aggregate the feature information at each scale, which increased the variation of different riveting defects. Finally, the regression loss function of the original model prediction network was improved by using the linear combination of Smooth L1 and DIoU, which improved the detection accuracy and convergence speed of the DETR model for defect types. The experimental results show that the improved model exhibits high detection performance, with an average detection accuracy mAP of 97.12% and a detection speed FPS of 25.4 f/s for riveting defects. Compared with other mainstream detection models such as Faster RCNN and YOLOX, the improved model has significant advantages in detection accuracy and detection speed. The research results can meet the demand for real-time online detection of small-scale rivet riveting defects in large riveted parts under actual working conditions, and provide certain reference value for the application of vision detection technology in riveting processes. © 2024, Central South University Press. All rights reserved.