# Review on Rail Damage Detection Technologies for High-Speed Trains
## 2 Research Status of Non-Destructive Testing for Rail Damage
A rail surface defect detection method based on unmanned aerial vehicle images has a recall rate of 93.75% and an accuracy rate of 93.6%. By combining 3 DGIS and digital twin technology, air vehicle-ground collaborative inspection can be achieved to improve detection efficiency. However, in complex environments such as strong- or low-light conditions, defect features are easily masked during drone detection. The training data for foreign AI models are larger, and the algorithms are more mature. For example, JR Kyushu in Japan uses AMD Kria K26 system modules combined with FPGA technology to achieve high-speed image processing, with an accuracy rate of over 85% in rail sleeper damage classification. AMD Kria K26 system module was developed and launched by AMD (acquired Xilinx, San Jose, CA, USA) and released in 2021. It focuses on edge computing applications and aims to provide flexible and high-performance solutions for industrial Internet of Things, machine vision, medical imaging and other fields. Multi-sensor fusion technologies such as laser radar, infrared, and ultrasound are commonly used abroad. For example, the French TGV detection system integrates subsystems such as track geometry measurement and contact network detection to achieve the comprehensive monitoring of railway facilities. However, there are shortcomings in the depth of multi-sensor data fusion in China, and there is a lack of a unified standard framework. Electromagnetic testing is based on the principle of electromagnetic induction, mainly including magnetic particle testing, magnetic leakage testing, alternating current electromagnetic field technology (ACFM), and metal magnetic memory testing technology. In2014, [PERSON] et al. [26] used magnetic flux leakage testing technology to detect surface crack defects. In 2017, [PERSON] used the blind source separation method to process electromagnetic detection signals using BSS, which showed significant improvement compared with traditional methods [27]. Research has found that current railway track characterization defects such as rolling contact fatigue (RCF) cracks can be detected to a certain extent, but there are certain errors. For example, the ACFM cane manual rail inspection instrument has significant errors in detecting rail characteristic crack defects, with an automatic calibration error of about 20 mm for the robotic arm. It can be seen that researching and developing intelligent and automated railway characterization defect detection equipment systems and methods is currently an important task. At the same time, with the development of intelligent transportation technology, it is particularly important to ensure the service life and quality of high-speed train tracks. This requires the real-time monitoring and detection of the service life and quality of tracks, whether under conventional conditions or extremely harsh conditions. This will inevitably bring great difficulties to track inspection, increase the cost and difficulty of intelligent rail inspection, and further highlight the importance of intelligent, digital, and comprehensive rail quality inspection and monitoring equipment and methods.