电子测量与仪器学报

龙陵波,赵宏,杨聪,魏民,肖昌炎.铁路道岔参数机器视觉在位测量方法与装置[J].电子测量与仪器学报,2023,37(4):80-89

铁路道岔参数机器视觉在位测量方法与装置

Machine-vision based method and apparatus for in-situmeasurement of railway turnout parameters

DOI：

英文关键词:switch rail parameters machine vision camera self-calibration laser center extraction

基金项目:国家自然科学基金(2021JJ50159)、湖南省自然科学基金(Z202232400398)项目资助

作者	单位
龙陵波	1. 湖南大学机器人视觉感知与控制技术国家工程实验室
赵宏	2. 国防科技大学空天科学学院
杨聪	1. 湖南大学机器人视觉感知与控制技术国家工程实验室
魏民	1. 湖南大学机器人视觉感知与控制技术国家工程实验室
肖昌炎	1. 湖南大学机器人视觉感知与控制技术国家工程实验室

Author	Institution
Long Lingbo	1. National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University
Zhao Hong	2. College of Aerospace Science Engineering, National University of Defense Technology
Yang Cong	1. National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University
Wei Min	1. National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University
Xiao Changyan	1. National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University

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中文摘要:

道岔几何位置异常是导致列车脱轨的主要原因,对其进行实时监测是有效预防列车脱轨的重要手段。针对此需求,本文设计一套基于双目视觉的道岔关键参数在线原位监测装置。首先,针对铁路行车振动易造成视觉测量系统外参改变的难点, 提出一种基于简易标签的在线自标定算法。此外,为了准确定位道岔监测特征,采用激光标记方式增强纹理特征的方法,可解决尖轨摆动导致成像视角变化导致监测特征难以定位的难题。同时,针对露天光照的干扰,提出了高斯加权的灰度重心法提取光条中心,该方法成功克服铁轨金属表面易漫反射等缺陷,可有效定位出监测特征。最后,完成双目立体配合并计算出监测特征点三维坐标,完成道岔参数的监测。现场实测表明,本装置硬件成本低、鲁棒性强、速度快,误差约为 0. 3 mm。

英文摘要:

The primary factor for train derailing is the turnout’s aberrant geometric location, thus, it is crucial to monitor it in real time to effectively avoid derailing. This work develops a set of online in-situ monitoring systems for crucial turnouts parameters based on binocular vision to meet this need. First, an online self-calibration method based on straightforward labels is proposed to address the issue that the vibration of railroad traffic readily changes the exterior parameters of the visual measuring equipment. Additionally, laser marking is utilized to strengthen the texture characteristics in turnout monitoring features in order to properly detect them. This can resolve the challenging issue of monitoring features locating following the change of imaging viewpoint produced by the swing of the sharp rail. The Gaussian-weighted grayscale center of gravity approach is proposed to extract the center of the light strip for outdoor light interference. Our method successfully overcomes challenges such easy diffuse reflection on the metal surface of the rail and can accurately find monitoring features. Binocular stereo collaboration is accomplished, and then the monitoring of turnout parameters is finally completed by calculating the spatial three-dimensional coordinates of monitoring features. The field real measurement demonstrates that this device has low hardware cost, high resilience, and rapid speed, with an inaccuracy of approximately 0. 3 mm.

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