# Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects
## 1 Introduction  
The gradual increase in rail surface defects in recent decades may be attributed to the aging of urban railway rails. In South Korea, the current practice for evaluating rail surface damage involves visual inspection by engineers and simple measuring tools based on the Detailed Guideline on the Performance Evaluation of Track Facilities. A significant budget has recently been allocated in accordance with the enactment of the Detailed Guideline on the Performance Evaluation of Track Facilities in South Korea. In accordance with the recent enactment of the Track Diagnosis Act in Korea, the performance of railway facilities is comprehensively evaluated through field surveys and various tests to identify and predict the objective current status of railway facilities and future changes in performance. Through this, a large budget is being invested to help railroad facility managers determine the optimal time for repairs and improvements. Despite the rapid increase in rail diagnostics, it is difficult to ensure the reliability of diagnostic results through labor-intensive visual inspection techniques. Periodic track walk-arounds and visual inspections are crucial for detecting defects on the rail surface. However, assessing the severity of defects on the rail surface based on the inspector's subjective judgment is significantly constrained in terms of predicting internaldamage to the rail. A majority of the previous studies has focused on coating tests and numerical analysis related to the types of damage caused, dynamic behavior, and material characteristics of rail surfaces. However, there is a lack of research on internal defects associated with rail surface damage [1; 2; 3; 4]. [PERSON] et al. [1] proposed a dynamic model considering the interaction force between wheels and rails, and they qualitatively analyzed the dynamic behavior of ballast tracks. In addition, they analyzed the increase in dynamic wheel load with respect to the size of the load based on rail surface measurements. [PERSON] [5] analyzed the frequency range of the corrugation mechanism, concluding that trains moving at different speeds may generate corrugation of similar wavelengths at different locations owing to different mechanisms. They further investigated the characteristics, causes, and solutions of rail corrugation. [PERSON] et al. [6] performed a numerical analysis to examine the changes in contact shape and stress based on the contact position and wear between the wheel and rail. [PERSON] et al. [7] proposed a methodology for wheel-rail contact and analyzed various aspects, including the contact positions of the wheelset. They integrated a railway dynamics simulation model based on the hypothesis of elastic contact. [PERSON] et al. [8] conducted a study on RCF and wear behavior. They analyzed the impact of headcheck, which is a type of rail defect, on the contact between the wheel and rail in regard to causing defects and derailment. Furthermore, the application of rail surface coating using two new surface materials on the rail head has helped in improving the durability, lifespan, and resistance to the RCF of the rail. [PERSON] [9] studied contact fatigue and the applicability and limits of fracture-mechanics-based life predictions. [PERSON] et al. [10] performed several experimental investigations on the competitive relationship between wear and RCF in the wheel-rail interface, providing evidence for rails. [PERSON] [11] conducted a literature review on the grinding and milling techniques for railway maintenance, with the aim of analyzing the operational principles as well as the advantages and disadvantages of each technique. Based on this analysis, [PERSON] proposed improvements to the current grinding and milling approaches, aiming to enhance the railway maintenance strategies. The analysis indicates the massive potential of the concept of milling and high-speed polishing.