基于机器视觉的轨道交通接触网旋转双耳状态检测方法
杨红梅
State Detection Method of Rail Transit Catenary Rotary Double Ears Based on Machine Vision
YANG Hongmei
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作者信息:四川铁道职业学院铁道工电学院， 611732， 成都
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Affiliation:School of Railway Engineering and Electricity, Sichuan Railway College, 611732, Chengdu, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2023.09.045
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中图分类号/CLCN:U226.8
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栏目/Col:应用技术
摘要:
目的：为实现轨道交通接触网支撑装置旋转双耳耳片断裂状态自动识别，需研究相应的检测方法进行识别，以在设备故障时及时更换维修，从而提高接触网系统腕臂结构稳定性。方法：首先利用Hough变换对接触网支撑装置的全局图像进行预分类，将待检测图像的基本图元分为杆状物、旋转双耳和绝缘子三大类；进而通过仿射不变矩初识别旋转双耳类；然后实现旋转双耳耳片的精确定位；最后对耳片局部图像通过参数矩阵极值分布规律识别断裂特征。结果及结论：提出了基于机器视觉的状态检测方法，该方法能较准确地识别支撑装置旋转双耳耳片的故障特征，同时检测时间短，识别率高。特别对于重点检测区段，也可基于本方法通过数据处理中心进行线下再评估，以满足系统可靠性要求。无论在线检测还是离线检测，该方法均可满足接触网零部件的检测监测需要。
Abstracts:
Objective: To automatically identify the fracture state of rotary double ear pieces for rail transit catenary support device, it is necessary to develop adequate detection methods for identification, and enable timely replacement and maintenance in case of equipment failures, thereby improving the stability of catenary system arm structure. Method: Firstly, the global image of catenary support device is preclassified using Hough transform, dividing the basic elements of the image for detection into three categories: rodshaped objects, rotary double ears and insulators. Then, the category of rotary double ears are primarily identified using affine invariant moments; subsequently, the rotary double ears are accurately located; and finally, the fracture features of the ears in local images are identified through the distribution of extremal parameter matrices. Result & Conclusion: The state detection method based on machine vision is proposed, which can accurately identify the fault features of support device rotary doubleear pieces, with shorter detection time and higher identification rate. Specifically, for the key detection sections, offline reevaluation can be conducted based on this method through a data processing center to meet the system reliability requirements. This method can fulfill the detection and monitoring needs of catenary components regardless of online or offline detection.
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