# Review on Rail Damage Detection Technologies for High-Speed Trains
## 5 Conclusions
Through the comprehensive and in-depth perception of rail operation detection data, the real-time transmission and exchange of rail detection data, and the rapid analysis and modeling of data, it is the transformation and embodiment of the rail inspection system to achieve digitization, intelligence, intelligent interaction, and wireless remote intelligent interaction. In the future, rail inspection intelligent systems with wireless remote intelligent interaction will be an inevitable development trend. (4) Real-time online status monitoring and digital and intelligent operation and maintenance of railway systems. With the deepening of research on high-speed and ultra-high-speed train technology, damage detection in track infrastructure such as roadbeds, rail fasteners, and vehicles, in addition to railway tracks, will undoubtedly be a key direction of future transportation research. How to use more digital and intelligent methods to solve real-time online rail status monitoring and ultimately achieve intelligent operation and maintenance is a key and difficult point of research. At the same time, it is necessary to consider the impact of resonance on railway tracks, vehicles, and roadbeds, as well as the analysis, processing, and feedback timeliness of real-time online status monitoring data of railway tracks, vehicles, and roadbeds. Based on this, the development of non-destructive testing methods and equipment based on intelligent algorithms for the online status monitoring of railway track damage and intelligent operation and maintenance algorithms under multi-source data signals is a key direction and a trend for the future development of high-speed and ultra-high-speed train technology. (5) Intelligent and digital detection methods for rail damage. Domestically, unmanned aerial vehicles have been equipped with technologies such as LiDAR and AI image recognition to detect track geometry parameters and surface defects. The surface defect detection method for steel rails based on drone 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, significantly improving efficiency. The highest detection speed of the domestically produced GTC-80-II-J rail flaw detection car is 80 km/h, with a damage detection rate of over 85%. Combined with 3D laser track profile measurement technology, the position of the probe wheel is dynamically adjusted, and the error is controlled at the millimeter level. The vehicle is equipped with 18 probe chips and 26 ultrasonic channels, which can detect internal cracks in the steel rails in all directions. AI and big data analysis: The 4C contact network detection system analyzes more than 10 million images through AI algorithms, and the number of defect detection operations is more than 10 times that of manual labor, with significantly better accuracy than manual labor. The intelligent detection system for railway sleepers can identify fine cracks at the 0.1 mm level and monitor production environment safety indicators in real time. The track inspection car adopts dynamic adjustment technology, with a track direction/height standard deviation of \(\leq\)0.3 mm, meeting the smoothness requirements of high-speed railways. Some railway bureaus are piloting predictive maintenance systems based on digital twins, which combine historical data to predict rail wear trends and optimize maintenance cycles. (6) Future challenges in track damage detection. In terms of future challenges, complex environmental interference, rust, oil stains, and changes in lighting on the surface of steel rails can lead to misjudgments in image recognition. Drone detection can easily mask defect features in strong- or low-light environments. Electromagnetic interference and terrain obstruction affect sensor signal transmission, leading to a decrease in GPS positioning accuracy for mountainous railways.