铁道科学与工程学报2024，Vol.21Issue(4)：1690-1700,11.DOI:10.19713/j.cnki.43-1423/u.T20230976
基于改进DETR的机器人铆接缺陷检测方法研究
Research on robot riveting defect detection method based on improved DETR
摘要
Abstract
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.关键词
铆接缺陷检测/DETR/EfficientNet/3-D注意力机制/多尺度加权特征融合Key words
riveting defect detection/DETR/EfficientNet/3-D attention mechanism/multi-scale weighted feature fusion分类
信息技术与安全科学引用本文复制引用
李宗刚,宋秋凡,杜亚江,陈引娟..基于改进DETR的机器人铆接缺陷检测方法研究[J].铁道科学与工程学报,2024,21(4):1690-1700,11.基金项目
国家自然科学基金资助项目(61663020) （61663020）
甘肃省高等学校产业支撑计划项目(2022CYZC-33) （2022CYZC-33）
兰州交通大学军民融合创新团队培育基金资助项目(JMTD202211) （JMTD202211）
兰州交通大学"百名青年优秀人才培养计划"资助项目 （）
