太赫兹科学与电子信息学报2024，Vol.22Issue(11)：1221-1227,1269,8.DOI:10.11805/TKYDA2023253
基于改进Faster R-CNN的高铁扣件弹条缺陷检测
Fastener clips defect detection based on improved Faster R-CNN in high-speed railway
摘要
Abstract
In response to the difficulty in detecting defects in high-speed rail clip springs caused by complex lighting environments,an improved Faster Region Convolutional Neural Networks(R-CNN)-based defect detection method for clip springs is proposed.By extracting defect feature maps through multi-layer convolutional neural networks,the network's attention to defect features is enhanced,and the impact of interference from complex lighting environments is reduced.A region proposal network is designed to generate candidate regions,and based on these regions,pooling is performed to extract the corresponding specific defect locations in the feature maps.The fully connected layers of the region proposal network are employed to calculate the specific categories and precise locations of defects,yielding the final detection results.The proposed algorithm can fully suppress the interference of lighting environments,significantly enhance the representation ability of defect features,simplify the image pre-processing stage,and reduce the requirements for the quality of the original image.Experimental results show that the proposed algorithm can effectively detect defects in high-speed rail clip springs,and compared to existing algorithms,it has a higher accuracy,stronger robustness,and significantly improved computational efficiency.关键词
缺陷检测/扣件弹条/区域卷积神经网络/区域候选网络/图像噪声Key words
defect detection/fastener spring clips/region-based convolutional neural networks/region proposal network/image noise分类
信息技术与安全科学引用本文复制引用
梁楠,张伟,刘洋龙,荆海林..基于改进Faster R-CNN的高铁扣件弹条缺陷检测[J].太赫兹科学与电子信息学报,2024,22(11):1221-1227,1269,8.基金项目
河南省科学院科技开放合作基金资助项目(210907008) （210907008）
河南省科技攻关基金资助项目(232102210056) （232102210056）
河南省科技研发计划联合基金资助项目(235200810049) （235200810049）
