基于图像处理技术的车载钢轨波磨自动识别与预警系统
崔霆锐1,2李宇杰2刘畅2霍苗苗2
Automatic Identification and Early-warning System for On-board Rail Corrugation Based on Image Processing Technology
CUI Tingrui1,2LI Yujie2LIU Chang2HUO Miaomiao2
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作者信息:1.北京交通大学电气工程学院， 100044， 北京
2.北京市地铁运营有限公司， 100079， 北京
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Affiliation:1.College of Electrical Engineering, Beijing Jiaotong University, 100044, Beijing, China
2.Beijing Mass Transit Railway Operation Co., Ltd., 100079, Beijing, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.01.013
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中图分类号/CLCN:U213.4+2
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栏目/Col:研究报告
摘要:
［目的］目前地铁轨道日常巡检采用的人工检测和轨道检查车方式存在效率低、成本高等问题，已无法满足日益发展的轨道交通安全运营需求。对此，需要基于图像处理识别技术，研究车载钢轨波磨自动识别与预警系统。［方法］阐明了钢轨波磨自动识别的总方案，从钢轨表面图像的去噪与光照不均校正问题及钢轨表面图像的定位与分割、钢轨波磨区间的定位、钢轨波磨周期估计等方面，详细阐述了基于图像处理的钢轨波磨自动识别与评估方法。基于系统构成和逻辑架构从软硬件角度分别描述了病害自动识别与预警系统的构建，并介绍了该系统在北京地铁的示范应用情况。［结果及结论］该系统能实现对轨道波磨病害的实时检测、定位、评估及预警。示范应用测试结果表明：该系统的钢轨波磨病害识别准确率达到97%以上；与传统人工巡检和专用轨道检测车相比，检测效率极具优势。
Abstracts:
［Objective］ Current daily metro track inspections, including manual detection and track inspection vehicle method, suffer from issues such as low efficiency and high costs, failing to meet the increasing demands for rail transit operation safety. Therefore, it is necessary to develop an automatic identification and early-warning system for on-board rail corrugation based on image processing technology. ［Method］ The overall solution for automatic rail corrugation identification is elucidated, covering the denoising and uneven illumination correction of rail surface images, the positioning and segmentation of rail surface images, the localization of rail corrugation interval, and the estimation of rail corrugation cycles. The automatic rail corrugation identification and assessment method based on image processing is elaborated. The construction of the defect automatic identification and early-warning system is described from both software and hardware perspective based on system composition and logic architecture, and the demonstration application situation of the system in Beijing Subway is introduced. ［Result & Conclusion］ The system enables real-time detection, localization, assessment and early-warning of rail corrugation defects. Demonstrative application test results indicate that the system achieves an identification accuracy of over 97% for rail corrugation defects. In comparison to conventional manual inspection and dedicated track inspection vehicles, the system exhibits a significant advantage in detection efficiency.
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