面向轨道安全巡检的全断面表观高速感知方法OA北大核心
A Method of Full Section Surface High-speed Sensing for Track Safety Inspection
轨道安全巡检要素多,基于单张影像的巡检方法垂直拍摄轨道局部影像,无法快速感知轨道全断面表观细节信息,多视角影像融合受轨检车转弯与晃车影响大,且不同视角内物体的反射强度差异导致影像存在明显亮度偏差,降低病害识别准确度.针对上述问题,研究了基于多线阵影像融合的轨道安全巡检全断面表观感知方法.使用棋盘格标定板建立各相机刚性连接关系,调整横向分辨率和纵向分辨率相等.分析不同类型轨道中的相对不变量,建立钢轨轨面和轨底边缘不变特征,使用改进的YOLO v10算法自动提取钢轨不变特征,利用不变特征计算直线段和弯道段、晃车段的像素偏差,实现影像高精度拼接.根据轨检车前进方向影像的像素统计特征,以亮度差异不大的两侧钢轨为约束,使用改进直方图匹配方法恢复图像整体亮度,实现接缝处均匀过渡.在武汉地铁、武汉局某工务段进行数据采集,轨检车以10、40、60、80、120 km/h的速度运行,使用本文提出基于YOLO v10的影像偏移恢复方法修正图像,查准率95.20%,召回率93.58%,平均精度84.03%,其中召回率较现有最优方法提升了0.51%;对图像关系恢复的平均误差为11.21 px,80 km图像修正合格率为99.43%;经过亮度调整的整体影像与约束影像像素均值差异为0.682,标准差差异仅0.344,较现有方法分别提升了61.38%和83.38%.实验结果表明:所提面向轨道安全巡检的表观细节感知方法,能够在轨检车以高速运行的条件下,获取位置误差小于4.5 mm、3σ置信度下灰度误差小于1.71的轨道全断面高分辨率影像,极大地提高轨道安全巡检效率.
Inspection of rail transit safety includes many elements.Methods based on a single image cannot per-ceive the details of the entire track section quickly.Multi-image stitching is greatly affected by the turning and swinging of the inspection vehicle,and the exposure conditions of different cameras result in significant brightness deviations,which reduces the accuracy of the identification of diseases.A full section surface sensing method for track inspection based on multiple line array images is proposed to address the above issues.A checkerboard calibra-tion board is used to establish a rigid connection relationship between each camera,and the horizontal and vertical resolutions are adjusted to be equal.An invariant feature of tracks is established based on the edges of track surface and bottom to calculate the deviation of straight,curved,and swinging sections.Improved YOLO v10 algorithm is used to automatically extract the above feature.An improved histogram matching method is used to restore the over-all brightness and achieve uniform transition at the joint of images based on the statistical characteristics of the for-ward direction and constrained by the two sides of the tracks with little brightness difference.Data collection is con-ducted at a certain section of Wuhan Metro and Wuhan Railway Bureau,with inspection vehicle running at speeds of 10,40,60,80,and 120 km/h.The restoration method for image offset based on improved YOLO v10 reaches an average error of 11.21 pixels,with a precision rate of 95.20%,a recall rate of 93.58%,and an average accuracy of 84.03%,in which the recall rate has an increasing of 0.51%,and a pass rate of 99.43%for 80 km.The mean differ-ence between the overall image and the constrained image after brightness adjustment is 0.682,and the standard de-viation is only 0.344,which are improved by 61.38%and 83.38%respectively compared to existing methods.The experimental results show that the proposed surface detail perception method for track safety inspection can obtain high-resolution images of the entire track section with a position error of less than 4.5 mm and a grayscale error of less than 1.71 under high-speed condition,greatly improving the efficiency of track safety inspection.
丁建隆;金辉;李兆新;程志全;宋天浩;徐浩轩
城市轨道交通系统安全与运维保障国家工程研究中心 广州 510330||广州地铁集团有限公司 广州 510330广州地铁集团有限公司 广州 510330广州地铁集团有限公司 广州 510330广州地铁集团有限公司 广州 510330铁科院(北京)工程咨询有限公司 北京 100081武汉大学遥感信息工程学院 武汉 430079
交通工程
轨道交通轨道安全巡检线阵相机表观病害钢轨不变特征
rail transittrack safety inspectionline array camerasurface diseasesinvariant characteristics of track
《交通信息与安全》 2025 (3)
24-32,9
国家重点研发计划项目(2023YFB2603702)资助.
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