Abstract
A point machine’s gap is an important indication of its healthy status. An edge detection algorithm is proposed to measure and calculate a point machine’s gap from the gap image captured by CCD plane arrays. This algorithm integrates adaptive wavelet-based image denoising, locally adaptive image binarization, and mathematical morphology technologies. The adaptive wavelet-based image denoising obtains not only an optimal denoising threshold, but also unblurred edges. Locally adaptive image binarization has the advantage of overcoming the local intensity variation in gap images. Mathematical morphology may suppress speckle spots caused by reflective metal surfaces in point machines. The subjective and objective evaluations of the proposed method are presented by using point machine gap images from a railway corporation in China. The performance between the proposed method and conventional edge detection methods has also been compared, and the result shows that the former outperforms the latter.
Highlights
The railway system in China has undergone a dramatic increase in recent years
We propose an improved gap measurement algorithm which integrates an adaptive wavelet threshold, local threshold in image binarization, and line structure element in mathematical morphology
The proposed method is tested on a number of point machine gap images recorded in a railway corporation in China, including low level noise images and noisy images
Summary
The railway system in China has undergone a dramatic increase in recent years. According to a report of the National Railway Administration of the People’s Republic of China [1], the passenger and cargo transportation volume were 2.535 billion and 3.358 billion tons, respectively, in 2015.Heavy traffic means that the capacity utilization of the existing infrastructure is high. The railway system in China has undergone a dramatic increase in recent years. According to a report of the National Railway Administration of the People’s Republic of China [1], the passenger and cargo transportation volume were 2.535 billion and 3.358 billion tons, respectively, in 2015. Heavy traffic means that the capacity utilization of the existing infrastructure is high. This will lead to more equipment failures and service disruptions. Among these equipment failures, railway point machines account for the vast majority of railway infrastructure failures that affect the availability of the system [2]. Almost 33% of the total maintenance cost of railways is dedicated to point machines and crossings [3]. How to monitor the health of railway turnouts and decrease their failure rates has become an important problem that urgently requires a solution
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