Mechanical Engineering Faculty Publications and Presentations

Document Type

Conference Proceeding

Publication Date

6-14-2024

Abstract

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.

Comments

Copyright © 2024 by ASME

Publication Title

Proceedings of the 2024 Joint Rail Conference. 2024 Joint Rail Conference.

DOI

10.1115/JRC2024-124640

Download

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.