# A Review of Deep Learning Applications for Railway Safety
## 4 Railway Infra Safety
### Rail Components  
Defect inspection of rail components (e.g., spikes that secure rails to ties and clips that press down on the bottom of the rail to concrete ties) commonly depends on the judgement of individual human inspectors. Many studies have used deep learning models to improve manual rail component inspection. [PERSON] et al. [58] proposed a framework that can detect pixel-wise rail accessories in real time using CNN-based models that receive high-resolution rail images, shown in Figure 5. Their proposed framework shows a speed of over 30 FPS in high-resolution processing video in real-time. These results show that inspection video can be quickly converted into helpful information to aid rail maintenance. Similarly, [PERSON] et al. [59] proposed CNN models to perform defect detection in rail ties and fixtures.  
[PERSON] and [PERSON] [60] developed a model that detects defects in rail dipped joints and track settlements and quantifies the degree of defects. Their proposed deep learning method receives 14 features, including weight, speed, and peak acceleration sensor data measured on wheels. The CNN and RNN modu  
Figure 5: Example of original image and label result: (**a**) ground truth; (**b**) instance label visualization. Reproduced with Permission from [PERSON] [58].  
series acceleration values, and the DNN modules used train weight, speed, and wheel acceleration feature point values.  
A train delivers high acceleration to wheelsets, axle boxes, the bogie, and total vehicle bodies as it passes through the rail. If defects occur in rail components, the acceleration data show different patterns. [PERSON] et al. [7] proposed a deep learning-based approach for defect detection in rail joints through CNNs on acceleration sensor data. CNN-based models can work directly with raw data to reduce the heavy preprocessing of feature engineering and directly detect joints located on either the left or the right rail. Similarly, [PERSON] et al. [61] used acceleration data to detect defects on rail joints. A single CNN model was designed to detect both left and right joints together. This can mitigate the interference issue when a different model is used for each side, which increases a high false-positive rate.  
A clamp is a rail component that ties a rail so it does not move from side to side. The clamp should maintain railway safety by maintaining the spaces on the left and right sides of the rail. Inspecting clamps is time-consuming and expensive because it depends on visual inspections made by a human expert. [PERSON] et al. [62] attempted to check clamps using two differential eddy current signals. The current signals were collected using sensors capable of measuring eddy-phase current signals of 18 kHz and 27 kHz and missing clamps in the fastening system were detected using machine learning algorithms.  
[PERSON] et al. [63] derived malfunction patterns of a rail switch machine. Mean, intermediate, maximum, and minimum values were extracted from current signals during the switch operation. Then, similar defects were formed into one group by using k-means clustering. The proposed model evaluated the performance by receiving current data generated during switch operation provided by the railway company and showed a high score (860) in the silhouette score, a clustering performance index.  
[PERSON] et al. [64] designed a real-time monitoring system to detect rail slab deformation of high-speed railways. This work combined fiber optic sensing methods and machine learning models to identify track slab deformation by using on-site track-side vibration acceleration data. The proposed method could identify the track slab deformation effectively and the detection rate could reach 96.09%.