摘要
随着深度学习技术在轨道巡检中的广泛应用,轨道扣件领域的视觉检测方法得到越来越多的研究。针对当前轨道扣件数据集中构建缺陷样本的效率瓶颈、以及基于图像数据展开部件松动检测手段相对匮乏的现状,本文提出了一种基于数据增强和YOLOv8模型的轨道扣件检测方法。本研究通过搭载于检测车上的线阵相机采集图像获取原始数据和纹理信息,利用图像的先验信息控制点云数据高效生成包含轮廓信息的掩膜图像及标签文件,基于风格迁移模型实现了纹理信息的迁移和融合。针对基于图像数据同步实现缺失等状态和松动状态检测的需求,引入注意力机制和自适应拼接层,构建多任务检测模型实现了扣件状态的快速识别与螺栓区域的精确分割,目标检测的平均精度达到了92.14%,语义分割的交并比达到了89.60%。本文方法有效提升了数据增强的效率,降低了二维图像领域对于螺栓状态的漏检概率。
With the wide application of deep learning technology in rail inspection,visual inspection methods in the field of rail fasteners have been increasingly studied.Aiming at the efficiency bottleneck of constructing defective samples in the current rail fastener data set,and the relative lack of means to detect loose parts based on image data,this paper proposes a rail fastener detection method based on data enhancement and YOLO model.In this study,the line array camera mounted on the inspection vehicle collects images to obtain raw data and texture information,uses the a priori information of the image to control the point cloud data to efficiently generate mask images and label files containing contour information,and realizes the migration and fusion of texture information based on the style migration model.Aiming at the demand of synchronization based on image data to realize the detection of missing and other states and loose states,the attention mechanism and adaptive splicing layer are introduced,and the multi-task detection model is constructed to realize the rapid identification of fastener states and the accurate segmentation of the bolt region,and the average accuracy of target detection reaches 92.14%,and the pixel accuracy of semantic segmentation reaches 89.6%.The method in this paper effectively improves the efficiency of data enhancement and reduces the probability of leakage detection for bolt states in the field of 2D images.
作者
刘亚好
任愈
Liu Yahao;Ren Yu(State Key Laboratory of Rail Transit Vehicle System,Southwest Jiaotong University,Chengdu 610031,China)
出处
《电子测量技术》
北大核心
2025年第21期207-214,共8页
Electronic Measurement Technology
基金
四川省科技计划资助项目(2023YFH0049)
四川省自然科学基金(2022NSFSC0415)项目资助。
关键词
轨道扣件
智能检测
数据增强
线阵相机
YOLOv8
rail fasteners
intelligent inspection
data enhancement
line array camera
YOLOv8
