# Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects
## 1 Introduction
[PERSON] [12] observed that the most common noise in railways is caused by the rolling contact of trains during operation, and the distribution of noise varies depending on the speed of operation. Their analysis revealed that the dynamic force between the wheels and rails has a significant impact on the noise generated in railways. [PERSON] et al. [13] conducted an analysis on the classification of rail defects caused by RCF. They reviewed the railway defect code system recommended by the International Union of Railways and analyzed the classification of rail defects caused by RCF. They further suggested the classification must adhere to the European standards. [PERSON] et al. [14] introduced the major defects associated with cracks and damages in railways, and they analyzed the load conditions and types of damage, such as contact, thermal stress, and residual stress, in addition to the scenarios of failure and the stages of crack propagation. [PERSON] [15] developed an automated rail inspection method to detect fatigue damage on the railhead surface, such as squats and headchecks. The experimental analysis demonstrated the effectiveness in terms of detection of rail defects using non-contact methods, such as laser ultrasound and EMAT, which utilize surface acoustic waves. [PERSON] et al. [16] explores the origins and impacts of out-of-round railway wheels, focusing on extended wavelengths, such as polygonalization, with 1-5 harmonics. Experimental detection techniques, mathematical models, criteria for wheel removal, and recommendations for mitigation are discussed. [PERSON] et al. [17] investigate the effects of lubrication in sharp curves on reducing friction, wear, and energy consumption between wheel and rail, confirming its benefits in preventing derailment and noise generation. [PERSON] [18] This paper discusses the dynamic loading of railway tracks and its impact on structural degradation, particularly at turnout crossings, proposing a methodology for monitoring deterioration in an urban rail network. [PERSON] et al. [19] employ deep learning techniques to detect and assess combined railway defects with high accuracy, utilizing simulated axle box accelerations for training and evaluation. [PERSON] et al. [20] highlight the importance of understanding wheel-rail dynamic interaction for enhancing railway capacity, discussing consequences, modeling approaches, detection methods, and proposed strategies for maintenance targeting weak track spots. In this study, rail samples were collected through field surveys in sections where rail surface defects occurred. Through an on-site investigation of the target section, several areas with typical damage were discovered, and a total of 20 m@3 rails were cut and collected. It was transported to the experimental space through night work. Additionally, the target section was replaced with a new rail. The collected rails were processed into various specimens focusing on areas with representative damage. The data acquisition process involved a detailed investigation of rails moved to the laboratory; therefore, surface image data was obtained, and the corresponding internal data was processed (SEM-Test) to obtain an image of the inside of the rail. In addition, the rail internal damage characteristics were analyzed through an indoor test (SEM test). Learning data were constructed based on rail surface defect images through field investigation and internal rail damage data through SEM testing, while a deep learning algorithm was developed by classifying classes through Headcheck and Spalling. We developed our own deep learning model for rail surface damage diagnosis using the Fast R-CNN algorithm. Through this study, a deep learning model was developed using learning data to indicate the state of cracks inside the rail as a more quantitative indicator, using only the rail surface image to derive internal rail defects that are difficult to investigate for inspectors.