# A Review of Deep Learning Applications for Railway Safety
## 4 Railway Infra Safety
### Rail Surface
[PERSON] and [PERSON] [9] analyzed acoustic emission signals from fatigue cracks on rails and developed a non-destructive localization model that determines the presence and location of defects without damaging railways. A DNN structure consisting of three hidden layers used the hyperbolic tangent function for considering the transient nature of acoustic emission signals. The model processed the acoustic emission signals and classified them into breaks at the head, web, or foot of the steel rail. [PERSON] and [PERSON] [52] developed a neural network model that predicts wheel and rail wear using an artificial neural network. Nonlinear autoregressive models with an exogenous input neural network (NARXNN) were developed for wheel and rail wear prediction. Wheel and rail profiles, plus load, speed, yaw angle, and first and second derivative of the wheel and rail profiles, were used as inputs to the neural network while the neural network output was wheel and rail wear. Their laboratory tests confirmed the feasibility of the proposed wear prediction methods for realistic wheel and rail profiles and materials. Studies have also been conducted to facilitate the acquisition and utilization of rail surface data necessary for artificial intelligence models. [PERSON] et al. [53] attempted to develop a robust detection framework for the quality and sampling rates of rail surface images. Unmanned aerial vehicles (UAV), capable of moving at speeds ranging 2-15 m/s, were used to collect rail images. In addition, the proposed model used enhanced residual blocks for time and memory optimization in defect detection. Two image datasets from high-speed train sections between Beijing and Shanghai and Class I freight lines in South Carolina were used for training and testing the model. [PERSON] et al. [54] proposed an efficient learning method based on line-level labels. Use of line-level labels can decrease the time and effort needed to collect data compared to pixel-level labels. In addition, this method can lower the model complexity and is more suitable for small data. The proposed model converted color information into numeric vectors using a 1D-CNN and LSTM, and detected rail surface defects line by line. [PERSON] et al. [55] focused on the data imbalance in detecting rail surface defects. Most rail  
Figure 4: SegNet architecture for rail defect analysis. Reproduced with permission from IEEE [50]. image datasets have an overwhelming proportion of normal state data than abnormal data including defects. Many captured images are not labeled to indicate whether they contains defects or not. [PERSON] et al. [55] proposed semi-supervised learning methods to detect defects on rail surfaces. The proposed semi-supervised learning methods showed compliance performance, more than other methods, to data imbalance, such as undersampling and oversampling. [PERSON] et al. [56] addressed degraded image quality due to substances, such as dust or oil, which often cause false-positive cases. A high-resolution camera can also help deal with substances but leads to loss of time and additional costs in the railway maintenance process. [PERSON] et al. [56] presented hardware and software architectures to perform railway surface inspection using a three-dimensional (3D) laser camera and deep learning. The use of 3D laser cameras in the railway inspection process provided high accuracy rates in real-time. [PERSON] et al. [57] utilized train acceleration data to estimate the degradation of tram rails. Machine learning algorithms (Random Forest, SVM, and ANN) were trained and tested using Melbourne tram network data. The study results revealed that the proposed method allows for cost-effective maintenance strategies by reducing the time and effort in collecting data for evaluation.