# A UAV-Based Visual Inspection Method for Rail Surface Defects
## 1 Introduction  
Rail transportation plays a significant role in the development of economic and industrial growth, and the failures of railway facilities (such as defects on the rail surface) are directly related to catastrophic accidents [1]. With the development of high-speed and high-load rail transit, the probability of rail surface defects is increasing rapidly. In general, rail surface defects which include corrugations and discrete defects due to wheel-rail contact conditions are the most common forms of defects [2]. Corrugations arise from periodic slip of the wheel on the rail as trains run on tracks [3]. The discrete defects are generated on the rail surface in an apparently random manner, i.e., without periodic characteristics, as shown in Figure 1. Those defects might cause serious accidents, or may even result in a catastrophic derailment of vehicles. Thus, this paper mainly discusses the detection of surface discrete defects. Currently, there are regular inspections of tracks in order to maintain safe and efficient operation [4]. Historically, inspection tasks are performed by trained personnel, by walking along the tracks. However, the manual inspection is inappropriate due to its low-efficiency, lack of objectivity, and high false alarm rate. Furthermore, the results are seriously dependent on the capability of the observer to detect possible anomalies and recognize critical conditions [5]. Therefore, automatic and nondestructive inspection methods should be urgently developed. At present, nondestructive inspection methods have been widely developed in a variety of industry inspection applications, due to their high efficiency and high precision [6]. Several methods have been applied to rail defects inspection, such as acoustic emission inspection [7], electromagnetic inspection [8], ultrasonic surface waves inspection [9], and visual inspection(VI) [10; 11; 12]. Particularly, with the development of computer vision techniques, VI (visual inspection) has been widely applied. VI is the most notable method for the surface defect detection because of its high speed and low cost [13]. Some researchers have studied rail surface defects by VI [14; 15; 16]. The VI method is an attractive approach for discrete defect detection. According to the traditional VI approach for surface defects inspection, a high definition (HD) camera is used to capture rail images; it is embedded in a detection system installed under an inspection train. Currently, most related researches are based on this approach. However, it has inevitable drawbacks, such as limited detection range, high cost, and so on. Inspection trains have to run over significant distances to capture rail images for a wide range of detection. The detectable parts and viewing angles are limited, especially for mountainous areas or across rivers. Unmanned Aerial Vehicles (UAVs) have become a research hotspot in many fields. The UAV-based inspection scheme is efficient and cost-effective, and has become attractive for change inspection in small-scale regions [17]. With the rapid development of UAVs, UAV-based aerial photography has been widely employed for engineering surveying and mapping [18], crop measurements [19], wind turbine blade surface inspection [20], power facilities inspection [21], historical buildings inspection [22], forest fire detection [23], bridge crack detection [24], fault detection in photovoltaic cells [25], and other detection applications such as target tracking [26], tracking and classification of multiple moving objects [27] and object recognition [28], etc. In general, UAV-based aerial photography has been extensively applied in various industries due to its advantages: low cost, ease of control, and flexibility. As mentioned, an inspection method of rail surface defects based on UAV combined with VI is proposed in this paper. A typical approach to detect surface defects is to automatically extract defects after image enhancement [15].