Detection of Rail Line Track and Human Beings Near the Track to Avoid Accidents

TL;DR

This paper introduces a real-time deep learning system using YOLOv5 for detecting railway tracks and nearby humans to prevent accidents, demonstrating significant accuracy improvements in safety monitoring.

Contribution

The paper presents a novel real-time railway safety system leveraging YOLOv5 for accurate detection of tracks and humans, enhancing accident prevention capabilities.

Findings

High accuracy in track detection

Effective identification of humans within one meter

Improved safety alert responsiveness

Abstract

This paper presents an approach for rail line detection and the identification of human beings in proximity to the track, utilizing the YOLOv5 deep learning model to mitigate potential accidents. The technique incorporates real-time video data to identify railway tracks with impressive accuracy and recognizes nearby moving objects within a one-meter range, specifically targeting the identification of humans. This system aims to enhance safety measures in railway environments by providing real-time alerts for any detected human presence close to the track. The integration of a functionality to identify objects at a longer distance further fortifies the preventative capabilities of the system. With a precise focus on real-time object detection, this method is poised to deliver significant contributions to the existing technologies in railway safety. The effectiveness of the proposed method is demonstrated through a comprehensive evaluation, yielding a remarkable improvement in accuracy over existing methods. These results underscore the potential of this approach to revolutionize safety measures in railway environments, providing a substantial contribution to accident prevention strategies.