# Review on Rail Damage Detection Technologies for High-Speed Trains
## 2 Research Status of Non-Destructive Testing for Rail Damage  
As railways are an important means of transportation, track life detection and reliability analysis have gone from manual inspection to a series of detection methods and means such as radar technology detection, eddy current detection, machine vision, ultrasonic detection, etc. For common defects, cracks, and fasteners on the surface of railway tracks, detection is still quite time-consuming. In recent years, with the rapid development of computer technology, image detection methods based on computer vision technology and deep learning have been used for railway inspection. For example, [PERSON] et al. [1] proposed a method for region de  
Figure 1: Comparison of shortcomings of common rail damage detection methods. Figure 2: Passenger and freight volume of railway national railways in the past 5 years. tection with CNN features (R-CNN), which has significantly improved the accuracy of object detection and is considered a major breakthrough in the field of object detection. [PERSON] et al. (2018) constructed a Feature Pyramid Network (FPN), which is a universal feature pyramid extraction method located in deep convolutional networks, to help detection models detect feature objects on a large scale. In terms of railway maintenance and upkeep equipment, developed Western countries often use handheld systems (such as ultrasonic speed measuring rods) or high-speed dual-use railway/road vehicles equipped with various non-destructive testing sensors for manual inspection. However, the railway standards and maintenance standards of different countries are different, which also leads to some difficulties and challenges in the inspection process of railway maintenance and upkeep equipment. Below, Figure 3 shows the detection standards for the direct relationship between railway track operating speed and tonnage in the UK. The train operation management automation system (COMTARC) of Japan's Shinkansen railway mainly uses fiber optic sensors to monitor foreign object intrusion limits. Each monitoring point determines whether intrusion limits have occurred and the degree of harm through the attenuation of optical signals. When the alarm control conditions are reached, there is no need for dispatch personnel to manually confirm, and the train speed limit control is immediately activated ([PERSON], 2017). The continuous real-time tracking automation system (ASTREE) and train automatic control system (TVM-430) ([PERSON], 2017) of French railways mainly achieve the function of foreign object intrusion limit detection by setting up a protective monitoring alarm network. When the alarm control conditions are reached, the train speed limit is achieved, simultaneously using radar technology to detect whether passengers have intruded into the track space on the platform and issue an alarm. With the development of artificial intelligence technology, the combination of artificial intelligence and robotic technology can minimize the errors caused by manual operations in railway inspections. As shown in Figure 4, the ACFM ([PERSON], 2017; [PERSON], 2017) cane manual rail detector is used. The ACFM probe inevitably causes lifting changes or magnetic changes along the inspection direction (due to residual stress) at higher speeds, which may lead to an increase in background signals. If the threshold is set too low, it may result in certain defects not being detected. On the other hand, setting the threshold too high depending on the SNR may result in multiple false alarms. Preliminary investigations into low-speed and high-speed ACFM measurements on steel rails indicate that it is difficult to adjust the threshold to detect all defects. Figure 3: Test standard for the direct relationship between railway track speed and tonnage in the United Kingdom. In recent years, China's railway inspection technology has also developed rapidly.