# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 1 Introduction  
Rail corrugation refers to the periodic non-uniform plastic deformation and wave-shaped irregularities along the longitudinal surface of rail heads [1]. In recent years, the development of urban rail transit has been severely affected by the problem of corrugation on the metro operation tracks. Due to the occurrence and evolution of rail corrugation, not only will the sudden changes in wheel-rail forces shorten the service life of the critical components of the rail line, which increase the maintenance costs but also the violent vibration between wheels and rails will generate great noise, reduce the passenger comfort and the safety of vehicle operation [2]. Corrugation detection has attracted more and more attention in recent years [1, 3, 4]. Therefore, it is of great significance to inspect corrugation in time and alleviate the development of corrugation effectively, which can reduce the cost of railway maintenance and ensure the safety of railway traffic. For a long time, the detection methods for rail corrugation mainly based on manual inspection with a particular calliper or a corrugation inspection car. However, the fixed detection cycle cannot inspect the track state in time and this results in low detection efficiency [3]. Therefore, a growing number of research studies have been focused on improving the detection rate and accuracy. Nowadays, researchers have proposed a variety of new non-contact corrugation detection methods, including the chord surveying method, inertial reference method, computer vision method andso forth [5; 6; 7]. Among them, the first two methods are mostly based on mechanical and physical mechanisms, which are easy to calculate and handle but there are various factors to be considered, the anti-interference ability is insufficient and they are not efficient enough for a routine inspection. Conversely, with the rise of computer vision technology, the image processing technique has made significant achievements in the track inspection device, which has the advantages of being non-contact, low cost and fast. In addition, it can achieve multiple target detections simultaneously and analyze the images for a track field to figure out rail defects with advanced pattern recognition algorithms. Hence, it is remarkably suitable to performing long-term, high precision and repetitive tasks under complex environmental conditions [8; 9; 10; 11]. At present, the vehicle track inspection system based on computer vision, mainly for rail fasteners and surface defects, has been used in some countries and the corresponding identification methods have been investigated and applied. Nevertheless, there are only a few reports on rail corrugation identification based on computer vision. [PERSON] et al. proposed a rail corrugation identification method based on spatial filter characteristics and a machine learning algorithm [12]. The Gabor texture features are first extracted from the entire rail image and then the K-nearest neighbor method is used for corrugation recognition, in which a high recognition precision is obtained. It has become a widely used corrugation identification method based on computer vision [13] but it is sensitive to disturbances such as rust on the rail surface. [PERSON] et al. proposed an identification method for rail corrugation based on rail image features in the frequency domain, including the image acquisition subsystem and the corrugation identification subsystem installed under the vehicle body [14; 15]. Instead of global texture features, local frequency features used in this method can reduce the time-consuming problem of traditional image methods and further improve the identification accuracy. However, it is still only able to detect the existence of corrugation and cannot analyze the periodicity and severity of the corrugation. Therefore, it is necessary to make some further efforts on this issue. These motivate the following works. In this paper, the rail corrugation identification is converted into an image classification problem.