Grade crossings are critical elements of the railway infrastructure due to the potential risk of vehicle collisions with trains. According to the National Highway Traffic Safety Administration, there were more than 1,600 vehicle-train, and 500 human-train collisions in 2020. Researchers, transportation organizations, and government bodies are constantly exploring practices and technologies to improve safety at crossings. Examples of safety standards include sensors, motion detectors, depth cameras, and many other innovative technologies. The goal of this paper is to investigate the applications of computer vision using Artificial Intelligence (AI) deep learning models to enhance railway safety. Deep learning models can provide a robust and cost-effective solution to detect multiple hazards at crossings under different conditions such as lightning and weather. To achieve that goal, a model is developed and trained using a dataset of images at crossings collected and labeled by the authors. The trained model can use footage from cameras installed at grade crossings to detect multiple hazards such as vehicles, pedestrians, and animals. The model can be a robust solution to provide uninterrupted monitoring of crossings.

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