# A UAV-Based Visual Inspection Method for Rail Surface Defects
## 1 Introduction
The most two popular methods for image enhancement  
Figure 1: The discrete defects on the rail surface. are by the histogram equalization (HE) and homomorphic filtering algorithms. However, since the HE is a linear algorithm and only averages the gray level distribution rather than enlarging the gray scale, there are several shortcomings, such as loss of image detail information and noise amplification [29]. Homomorphic filtering algorithm based on the illumination reflectance model is a frequency-domain processing to compress image light regions and enhance contrast. This method makes use of the frequency information of images. However, it often blurs image details, leading to a lack of deliberation of the spatial local characteristics of images [29]. In addition, gray values in the global scope change dramatically because of the uneven illumination and reflectance properties of rail surfaces [14]. Therefore, the two global enhancement methods are not suitable for rail images. For defect detection, research on models that automatically locate defects after image enhancement has several achievements. For instance, a visual inspection system (VIS) is proposed for discrete defect detection [14]. In the VIS, images are captured by a high-speed digital camera fixed on a train. Subsequently, track extraction based on projection profile (TEBP) algorithm is used to extract the areas of rail track in images, and then the local normalized (LN) method and the defect localization based on projection profile (DLBP) method are applied to detect rail surface defects. The MLC-PEME model is proposed for defect detection [30]. Firstly, the histogram-based track extraction (HBTE) algorithm is used to extract areas of rail track in images which were captured by a camera fixed on an inspection train, then the MLC (Michelson-like contrast) combined with proportion emphasized maximum entropy (PEME) method is applied to detect defects. These methods performed well; however, they are seriously influenced by noise and background points, and consequently, have massive false detection rates [15]. An inverse PM diffusion model is proposed to enhance images [16]. Therefore, an adaptive threshold binarization is able to readily locate surface defects. However, it is seriously influenced by noise points, and yields in high false detection rates [15]. Most inspection models are based on the VI system fixed on inspection trains, and research on the UAV-based inspection of rail surface defects is rarely discussed. In this study, we faced the following serious challenges:  
* Rail position variances in UAV images. Unlike inspection trains, the camera angle of HD camera installed on UAV is sensitive to environment aspects (such as wind and turbulence) and operators. Although the UAV can balance itself by using GPS flight mode, rail positions in images captured by UAV aerial photography are extremely variable. Therefore, the variances of rail positions bring difficulties to rail extraction. * Non-uniform illumination and noise corruption. Due to partial occlusion of infrastructures around the rail (such as catenary etc. ), reflectance properties of rail surface and shake of the UAV and other environmental factors, the brightness and contrast of images are uneven and low. According to [17; 31], UAV digital images are likely to be corrupted by noises during the acquisition or transmission. In general, the gray levels of surface defects are lower than that of background (non-defect area) [14], but the order of these values is often broken because of non-uniform illumination and noise corruption, as shown in the Figure 1.
* Few characteristics for defects segmentation. A corrugation initiates and develops easily because of the periodic occurrence of contact vibration [32]. However, it is difficult to inspect discrete defects by the VI method due to the lack of periodicity. Surface defects have low grey-level, that distinguishes them from the dynamic background. Therefore, the grey-level is considered to be the most available feature [14].