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基于立体视觉技术的岔区钢轨爬行检测方案

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道岔区钢轨爬行病害会对铁路运营安全带来不利影响,当前人工检测手段存在巡检效率低、精度不高等问题.本文将立体视觉检测系统搭载到移动平台,开发钢轨爬行自动检测机器人、算法模型和检测标靶,通过定位钢轨标靶并利用立体视觉系统拍摄路旁标靶计算坐标的方式,实现了视觉系统在运动状态下的高精度检测.针对道岔结构特性设计了道岔区钢轨爬行检测方案.道岔区尖轨爬行检测试验表明,在5 km/h速度条件下,重复性试验最大误差为0.19 mm,准确性试验最大误差为0.54 mm,满足毫米级检测精度要求,可在2 min内完成整组道岔的检测.钢轨爬行检测机器人具有运行速度快、检测精度高的特点,可实现铁路钢轨爬行的大范围巡检,具有良好的应用前景.
Rail Crawling Detection Scheme in Turnout Area Based on Stereoscopic Vision Technology
The rail crawling disease in the turnout area will have adverse effects on railway operation safety,and the current manual inspection methods have problems such as low inspection efficiency and low accuracy.The stereoscopic vision detection system was installed on a mobile platform,and a rail crawling automatic detection robot,algorithm model,and detection target were developed.By locating the rail target and using the stereoscopic vision system to capture the coordinates of the roadside target,high-precision detection of the vision system in motion was achieved.A rail crawling detection scheme for the turnout area was designed based on the structural characteristics of the turnout.The crawling detection test of the switch rail in the turnout area shows that under the condition of 5 km/h,the maximum error of the repeatability test is 0.19 mm,and the maximum error of the accuracy test is 0.54 mm,which meets the millimeter level detection accuracy requirements and can complete the detection of the entire set of turnouts within 2 min.The rail crawling detection robot has the characteristics of fast running speed and high detection accuracy,which can achieve large-scale inspection of railway rail crawling and has good application prospects.

rail crawling detectionstereoscopic visionturnoutdetection robotdetection scheme

刘心成、杨维一、赵闻强、王艺臻

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中国铁道科学研究院集团有限公司 铁道建筑研究所,北京 100081

高速铁路轨道系统全国重点实验室,北京 100081

浙江省交通投资集团有限公司智慧交通研究分公司,杭州 310020

北京市轨道交通建设管理有限公司,北京 100068

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钢轨爬行检测 立体视觉 道岔 检测机器人 检测方案

中国铁道科学研究院集团有限公司项目

2022YJ112

2024

铁道建筑
中国铁道科学研究院

铁道建筑

北大核心
影响因子:0.623
ISSN:1003-1995
年,卷(期):2024.64(8)