Real-Time AI-Powered Railway Track Surveillance System

Author(s)	Athisaya Sivan, Sumi M
Country	India
Abstract	One of the most popular and economically significant forms of transportation in the world is railroads. However, track impediments including trespassers, stray animals, fallen debris, landslides, and stalled cars at crossings continue to pose significant safety concerns for railway networks. Signalling systems and human monitoring by loco pilots, which are constrained by reaction times, visibility limitations, and environmental disruptions, are the main components of conventional railway safety methods. This study suggests a Real-Time AI-Powered Railway Track Surveillance System that combines edge computing, computer vision, and deep learning to detect hazards in advance. The YOLO (You Only Look Once) object detection architecture, on which the system is based, allows for the quick and precise identification of track invasions. By concentrating just on the active track zone, a Region of Interest (ROI) masking technique is used to lower false positives. In high-speed train contexts, the suggested model improves situational awareness, lessens reliance on manual monitoring, and closes the detection-braking gap. The study shows how clever automation may lower the probability of accidents and greatly increase operational safety.
Keywords	Railway Safety, Deep Learning, Computer Vision, YOLO Framework, Real-Time Hazard Detection
Field	Computer Applications
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-09

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