摘要
轨道扣件缺陷是铁路安全运行的重大安全隐患,基于二维图像处理方法能检测扣件外观缺陷,但难以检测扣件结构缺陷,提出了一种3D线激光传感器的轨道扣件结构缺陷检测方法。首先,利用3D线激光传感器获取轨道点云,根据扣件高度快速定位扣件区域点云,利用PointNet++网络对扣件区域点云分割获得弹条点云;其次,将弹条点云映射至二维图像,在二维图像中提取弹条骨架,二维骨架融合至三维点云获得初始骨架,对每个初始骨架点云拟合截面圆,以各截面圆心作为弹条骨架精确表示弹条轮廓及空间结构;最后,提取弹条三维骨架的特征点,根据特征点构造扣压平面和计算弹条缝隙,基于弹条缝隙检测扣件结构缺陷。为了验证文中方法的有效性,以WJ-7、WJ-8、WJ-2型弹条扣件为对象测量弹条缝隙,并将文中方法测量的弹条缝隙与人工使用缝隙尺测量的真实值进行比较,单个扣件的测量误差在0.1 mm内,且文中方法对轨道油污、锈斑及环境有较好的鲁棒性;对批量扣件的结构缺陷检测,当测量误差允许在±0.1 mm时,扣件结构缺陷检测的准确率不低于95%。
Objective Rail fasteners play a vital role in railway infrastructure by securing rails to sleepers and preventing misalignment. Prolonged usage of these fasteners can lead to different types of defects, including visual defects such as missing, fractured, and misplaced fasteners, as well as structural defects like overly loose or tight fasteners. These defects can range from minor issues affecting passenger comfort to serious risks of derailment,posing significant safety concerns for railway operations. The use of two-dimensional visual imaging techniques allows for quick identification of visual fastener defects, while three-dimensional vision sensors capture color and depth images simultaneously. Implementing multi-modal image fusion methods helps mitigate environmental and illumination effects to improve the accuracy of visual defect detection. Three-dimensional structured light imaging aids in accurately capturing the 3D point cloud of the railway track, enabling the detection of structural defects using the fastener's spatial structure. However, further improvements are needed to enhance the accuracy and reliability of structural defect detection. As a result, a new detection approach for structural defects in railway clip fasteners based on 3D line laser sensors is proposed.Methods Initially, a 3D line laser sensor is employed to capture the point cloud of the railway track.Subsequently, the point cloud corresponding to the fastener area is swiftly identified based on the fastener's height, and the metal clip point cloud is separated from this region using the PointNet++ network. The clip point cloud is then projected onto a 2D image, from which the clip skeleton is derived. This 2D skeleton is then transformed back into the 3D point cloud to establish the initial clip skeleton, with each point being approximated by a circular cross-section to determine the clip skeleton's center representing the clip's outline and spatial arrangement. Following this, feature points of the clip's 3D skeleton are extracted,
作者
袁小翠
王咏涛
刘宝玲
侯迪波
江宗辉
YUAN Xiaocui;WANG Yongtao;LIU Baoling;HOU Dibo;JIANG Zonghui(School of Electrical Engineering,Nanchang Institute of Technology,Nanchang 330099,China;College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,China)
出处
《红外与激光工程》
EI
CSCD
北大核心
2024年第7期154-168,共15页
Infrared and Laser Engineering
基金
国家自然科学基金项目(62001202)。
关键词
轨道扣件
结构缺陷
松紧检测
弹条缝隙
骨架提取
railway fastener
structural defects
tightness detection
clip gap
skeleton extraction
