The detection of rail surface defects is an important part of railway daily inspection, according to the requirements of modern railway automatic detection technology on real-time detection and adaptability. This paper presents a method for real-time detection of rail surface defects based on machine vision. According to the basic principle of machine vision, an image acquisition device equipped with LED auxiliary light source and shading box has been designed and the portable testing model is designed to carry on the field experiment. In view of the real-time requirement, the method of extracting the target area from the original image is carried out without image preprocessing. The surface defects of the rail are optimized based on morphological process and the characteristics of the defects are obtained by tracking the direction chain code. It is demonstrated that the maximum positioning time of this proposed method is 4.65 ms and its maximum positioning failure rate is 5%. The real-time detection speed of this proposed method can reach 2 m/s, which can carry out real-time detection of artificial hand walking. The time of processing each picture is up to 245.61 ms, which ensures the real-time performance of the portable track defect vision inspection system. To a certain extent, the system can replace manual inspection and carry out the digital management of track defects.

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Abstract.A new method based on wavelet decomposition and principal component analysis (PCA) is proposed to solve the problem that the traditional method cannot effectively and quickly detect the rail fastening nut. Through the wavelet decomposition of the rail fastener image, the high frequency component of the image can be removed, and the noise can be reduced and the running time of the algorithm can be reduced. The principal component analysis method is used to reduce the dimension of the image, and the minimum distance classifier is used to detect the rail fastener. The experimental results show that the proposed algorithm can effectively detect the missing state of rail fastener, and the algorithm is robust to the occlusion of the noise image and rail fastener image.