# A Review of Deep Learning Applications for Railway Safety
###### Abstract  
Railways speedily transport many people and goods nationwide, so railway accidents can pose immense damage. However, the infrastructure of railways is so complex that its maintenance is challenging and expensive. Therefore, using artificial intelligence for railway safety has attracted many researchers. This paper examines artificial intelligence applications for railway safety, mainly focusing on deep learning approaches. This paper first introduces deep learning methods widely used for railway safety. Then, we investigated and classified earlier studies into four representative application areas: (1) railway infrastructure (catenary, surface, components, and geometry), (2) train body and bogie (door, wheel, suspension, bearing, etc. ), (3) operation (railway detection, railroad trespassing, wind risk, train running safety, etc. ), and (4) station (air quality control, accident prevention, etc.). We present fundamental problems and popular approaches for each application area. Finally, based on the literature reviews, we discuss the opportunities and challenges of artificial intelligence for railway safety. railway; railway safety; deep learning; AI application +
Footnote †: journal: Accepted: 4 October 2022  
Additionally, safety accidents during train operation can occur, such as railroad trespassing or derailment. Such train accidents during train operation can lead to many casualties and various artificial intelligence technologies for train operation safety have been studied. For example, segmentation methods based on deep learning models have been proposed to detect rail tracks during operation or to check the existence of any obstacles on railways (e.g., people, cars). In addition, there have been attempts to quantify wind risk or train running safety for controlling and managing further operation. Finally, many studies have been conducted wherein artificial intelligence is used for railway station safety. A railway station is dynamic and complex due to the presence of many people, including passengers and station staff, and trains that stop and depart quickly. Therefore, it is necessary to prevent and deal with various safety accidents that may occur in stations. For example, diverse artificial intelligence models were developed to quickly identify three different types of safety incidents (fall, slip, and trip) [10] and to monitor air quality in a station. Furthermore, many studies have modeled the station as a dynamic and complex system. This paper provides a comprehensive view of railway safety by covering the four representative areas. Table 1 shows the railway safety areas addressed by earlier reviews. While most review papers have focused on a specific problem, few studies covered various areas. For example, [PERSON] et al. [11] and [PERSON] et al. [12] covered the four application areas we covered. However, these studies aimed to overview artificial intelligence for the railway, so some specific safety issues were rarely covered. This review more widely includes studies about railway safety. For example, it further covers safety issues related to the catenary in the railway infra, the train's door and suspension, and wind risk during the operation. In addition, although many studies have focused on visual inspection methods based on image data, this study includes diverse data types. For example, various sensor data (e.g., vibration, current, acoustic emission signals) and image-shaped data (e.g., 2D-camera images and laser ultrasound scanning data) are explained in this review. The subsequent parts of this review are organized as follows: Section 2 provides an overview of deep learning approaches that have been used for railway safety. We classified the deep learning methods according to their data source and task. In Section 3, we described the methodology for searching and analyzing related studies. From Section 4 to Section 7, we explained the four application domains in railway safety (i.e., railway infra, train, operation, and station) and representative studies.