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A Real Time Detection Method of Track Fasteners Missing of Railway based on Machine Vision

Volume 14, Number 6, June 2018, pp. 1190-1200
DOI: 10.23940/ijpe.18.06.p10.11901200

Hongfeng Maa, Yongzhi Minb, Chao Yinb, Tiandong Chengb, Benyu Xiaob, Biao Yueb, and Xiaobin Lic,d

aCollege of Electronic and Information Engineering, Lanzhou Institute of Technology, Lanzhou, 730050, China
bSchool of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China
cCollege of Software Engineering, Lanzhou Institute of Technology, Lanzhou, 730050, China
dJozef Stefan International Postgraduate School, Ljubljana, SI-1000, Slovenia

(Submitted on March 1, 2018; Revised on April 8, 2018; Accepted on May 20, 2018)

Abstract:

Detection of the missing track fasteners is an important part of the daily inspection of the railway, according to the requirement of real-time and self-adaptation of the modern railway to the automatic detection technology. A real-time detection method based on machine vision is proposed. On the basis of the basic principle of machine vision, the image acquisition device with LED auxiliary light source hood is designed. Adaptive image enhancement for fastener edge feature by using switching median filter and improved Canny edge detection method based on image gradient magnitude combined with the stability of the edge profile of fastener, real time detection of missing fastener has realized by template matching based on curve feature projection. After the experiment, the average processing time of each image is 245.61ms, the correct rate of recognition is 85.8%, and the method has a certain degree of adaptability, which supports up to 3.85m/s implementation speed and meets the real-time detection requirements for missing fasteners for actual operation of the real line. Rail damage detection method based on machine vision is often affected by noise interference in the process of image acquisition. In this paper, an improved median filtering algorithm is proposed to solve the problem of noise filtering appearing in the image. The algorithm points out an upper triangular block in the rectangular filter window as the mark point and finds the gray value in the upper triangular block to replace the gray value of the processing point. By the simulation experiment of this algorithm and other algorithms, the results show that the new algorithm is effective and the running time of the algorithm can be reduced effectively.

 

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