# A Review of Deep Learning Applications for Railway Safety
## 4 Railway Infra Safety  
### Catenary  
The catenary, which is responsible for supplying electricity to trains, is a critical facility in the electric railway system. Therefore, defects in a catenary can pose a severe threat to railway safety. While a human inspector usually needs to shut down the train power and go up to the vehicle to examine the state of the catenary, this procedure can cause many safety accidents. Prior studies have made efforts to study computer vision technologies to detect catenary defects fast and early. [PERSON] et al. [37] focused on detecting defects in the insulator, which is a catenary component. Figure 1 shows the proposed workflow of catenary defect detection. Their proposed framework captured images of areas where insulators are usually located using fixed-viewed cameras. Next, a Faster R-CNN model localized the specific location of the insulator in the input image (i.e., object detection). Finally, two other deep learning models were implemented to examine the extracted images of the insulator. One model was a deep learning classifier that had a CNN-DNN structure to output the classification score of the input image. The other model was an auto-encoder model that outputs the abnormal score of the insulator. The abnormal score determines whether and how the insulator is damaged. Actual data from Hefei-Fuzzhou high-speed railway line was used for evaluation. The results showed that the proposed framework effectively mitigates the small data problem and the complexity of processing catenary images, which can cause a decrease in diagnosis performance. There have been many studies on defect detection in a dropper, which connects a catenary and a messenger wire. [PERSON] et al. [38] proposed a method to detect defects in a dropper from image data by deep learning models based on Faster R-CNN and fully connected layers. A balanced attention feature pyramid network (BA-FPN) was proposed that integrates multiple-level features onto the original Faster R-CNN structure. This enhances detection performance by extracting useful image features from small areas from the entire catenary image where the dropper is placed. Experimental results on the VOC 2012 and MSCOCO 2014 datasets showed that the proposed models achieved higher performance than conventional detection models (86.8% at [EMAIL_ADDRESS] and 83.9% at [EMAIL_ADDRESS]). The clevis is another catenary component that is located between the registration arms and the cantilever. The Faster R-CNN has also been widely used to detect clevis defects. [PERSON] et al. [39] proposed a deep learning model that focuses on image features from the surrounding areas of the clevis, as shown in Figure 2. This idea is under the heuristic insight that the catenary has a typical structure, so there are specific areas where useful image features for clevis crack diagnosis are likely placed. The evaluation results reveal that the proposed model has higher crack detection performance than existing models, such as Faster R-CNN and YOLO. In addition, the proposed model was robust to different size, texture, and grayscale transformations that resulted from changes in shooting distance, angle, and illuminance. The split pin combines and supports diverse components in the catenary. [PERSON] et al. [40] studied a deep learning framework that determines three states of the split pin (missing, loosening, and normal) according to the location of the joint. First, the proposed framework  
Figure 1: Workflow of catenary defect detection. Reproduced with permission from IEEE [37]. performed an object detection task based on YOLO v3 to explore split pins for five joints extracted from the entire catenary image. Next, semantic segmentation was performed in three parts (head, body, and tail) using DeepLab V3+ [41; 42; 43]. Finally, the classification model determined the state of the split pins. The evaluation was conducted with 2670 catenary images, including 21,472 split pins, and the split pin defects were detected with very high accuracy (98.72%). [PERSON] et al.