Detection of Rail Fastener Based on Wavelet Decomposition and PCA

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.