基于机器视觉的轨道交通自动测距研究

doi:10.16180/j.cnki.issn1007-7820.2022.09.006

Abstract

Abstract:

A safe distance between two moving trains is an important condition to avoid train rear-end collision. Since the image data obtained by machine vision is rich in information and can be integrated in many aspects based on the collected images, this study proposes a distance measurement method based on monocular machine vision. This method uses the constant distance between the two tracks of the train (1 435 mm) as a benchmark to estimate the distance between trains. The images collected by the monocular camera are processed and analyzed by the convolution neural network to extract the track features. Based on the existing small hole imaging principle, the mapping relationship between the world coordinate system and the pixel coordinate system is derived, so as to optimize the calculation formula of the distance between trains. The experimental results show that the error rate of the system is less than 6%, and the measurement time of the system is within 40 ms, indicating that the method realizes the effective fusion and integration of ranging and other information obtained in the image, and can be used to judge the braking distance of the train.

Key words: distance measurement, machine vision, monocular camera, deep learning, image processing, track detection, pinhole imaging model, convolution neural network

CLC Number:

TP391

BI Jiazhen,SHEN Tuo,ZHANG Xuanxiong. A Research on Distance Measurement Between Trains in Rail Transit Based on Machine Vision[J].Electronic Science and Technology, 2022, 35(9): 37-43.

Figures/Tables 8

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References 18

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实际距离/m	测量结果/m	测量耗时/ms	错误率/%
40	38.2	35	4.50
60	57.5	36	4.17
80	77.2	32	3.88
100	97.1	37	2.90
120	116.5	34	2.92
140	134.5	35	3.86
160	153.1	38	4.31
180	170.7	34	5.17
200	188.2	31	5.90

A Research on Distance Measurement Between Trains in Rail Transit Based on Machine Vision

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