# Review on Rail Damage Detection Technologies for High-Speed Trains
## 3 Non-Destructive Testing Methods for High-Speed Train Rails
### Artificial Intelligence Detection Algorithms  
At present, in practical applications, including digital twin technology [86], traditional detection methods detect the surface defects of steel rails through manual visual inspection, rail inspection vehicles, physical methods, etc. The main problem is that during the process of manually inspecting or scanning the surface images of steel rails by rail inspection vehicles, complex and changing natural environmental conditions such as background conditions, light, and weather can interfere with the detection process to a certain extent, resulting in low image quality and subsequently affecting the accuracy of detection [87; 88]. When traditional methods cannot meet the requirements of automation and intelligence, convolutional neural networks are applied to track surface damage detection, opening up a new development path for detection work. To effectively compensate for the shortcomings of traditional image processing methods, many researchers suggest using deep learning methods to design fast and accurate track surface damage detection algorithms. [PERSON] et al. [89] improved the convolutional neural network by combining attention mechanisms, focusing the model on the defect areas on the surface of the rail. Using semantic segmentation methods, they achieved high segmentation accuracy for the contour segmentation of rail surface defects. [PERSON] et al. [90] used classical networks as the backbone extraction network and employed transfer learning methods to extract the surface defect features of steel rails. They designed adaptive edge and spatial feature extraction structures in the network to enhance the model's expressive power. [PERSON] et al. [91] used the Faster R-CNN object detection method to perform damage detection on B-type images of internal track damage. Although this method can effectively identify surface damage, its detection ability is single-focused on track damage due to the difficulty in obtaining track defect datasets. [PERSON] et al. [92] conducted defect detection on key components of railway tracks, which was mainly divided into two parts. Firstly, the position of the key components was recognized, and based on the recognized images, defect classification detection was carried out. The detection task was completed by segmenting the surface defect images of the steel rails using super-resolution algorithms. [PERSON] et al. [93] used deconvolution to reconstruct images and improve the classification performance of the model. The results showed that the surface reconstruction method was very effective in defect detection. [PERSON] et al. [94] used convolutional neural networks based on array methods to achieve the damage detection of rail scars and cracks. [PERSON] et al. [95] proposed the Wav2 Vec neural network for piezoelectric array ultrasonic guided wave localization based on ultrasonic guided wave SHM technology, achieving the accurate localization of rail damage and building an ultrasonic guided wave detection system. [PERSON] et al. [96] used fiber Bragg grating sensing technology to monitor rail damage and obtain data, addressing the problem that traditional high-speed railway sensors cannot cope with electromagnetic interference. They then used convolutional neural networks to detect the damage [97]. Considering the large number of rail point cloud data obtained through on-site scanning and the presence of a large number of noise points and outliers, a point cloud simplification algorithm was proposed to detect rails through direct filtering, maximizing the acquisition of detailed information about the rails. [PERSON] et al. [98] proposed a rail top defect detection method based on cascaded networks, targeting the characteristics of rail top defects. On this basis, deep learning algorithms were added to detect defects on the rail top surface.