# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 1 Introduction
Specifically, a learned classifier is used to determine whether the captured rail image is a corrugation rail image or not. At present, image classification methods based on local image descriptors are widely applied, which mainly includes Bag of Features (BOF) based method [16] and a Spatial Pyramid Matching (SPM) based method [17]. What is crucial to this kind of classification method is visual feature extraction and feature encoding process [18]. However, the traditional BOF or SPM only extracts a single feature of the image and the obtained feature information is not sufficient to adequately represent the rail image in many cases. In addition, by adopting the vector quantization (VQ) encoding method, the reconstruction error is large. These result in low image classification accuracy [19]. Therefore, taking the characteristics of corrugation rail image into account, the integrated idea is adopted to improve rail image classification performance. At first, to extract the image features more effectively, the Uniform LBP [20] and dense_SIFT [21] are used simultaneously to construct the feature set of the images. After that, to express the image information more specifically, codebooks for the two feature sets are created separately by using k-means clustering [22] to improve the robustness of the results. Finally, to reduce the loss of image information while improving the accuracy of the calculation, the LLC (locality-constrained linear coding) [23] is used to encode each image feature and further merged as the final image feature vector. In summary, an improved SPM model based on integrated multi-features and locality-constrained linear coding (IMFLLC) for rail corrugation identification is proposed. This method is expected to achieve a better identification result. In addition, as an important visual property of rail corrugation, period plays a vital role in further corrugation analyzing. Inspired by References [24; 25], a corrugation period estimation method in combination with image Fourier analysis and normalized distance matching function [25] (FFT-NDMF) is presented to achieve the automatic estimation of the corrugation period along the rail direction. It is useful to analyze these factors which result in the corrugation of the rail. To our best knowledge, this is the first paper handling the rail corrugation period estimation problem based on computer vision. Furthermore, valid and reliable corrugation severity assessment is helpful to make maintenance strategies for a lower maintenance cost and to extend the service life of the rails. Therefore, given the successful application of visual saliency in target detection and content analysis [26], a novel method for rail corrugation severity assessment based on synthesizing the visual saliency and fuzzy theory is proposed in this paper. The remainder of this paper is organized as follows. In Section 2, the problem solved in this paper is stated. In Section 3, the proposed rail corrugation identification method is presented in detail. In Section 4, the steps of the rail corrugation period estimation using the FFT-NDMF algorithm is investigated. After that, the method of the rail corrugation severity assessment is provided in Section 5. Finally, some conclusions of this paper are given in Section 6.