Automated Level Crossing System: A Computer Vision Based Approach with Raspberry Pi Microcontroller

TL;DR

This paper introduces an automated, computer vision-based system using Raspberry Pi microcontroller to detect trains and control level crossings, significantly enhancing safety and reducing accidents at unmanned railway crossings.

Contribution

It presents a novel deep learning-based automation system for level crossings using a microcontroller, integrating live video analysis and real-time notifications to improve safety and reduce costs.

Findings

Effective train detection and crossing control using computer vision

Reduction in level crossing accidents and fatalities

Cost-effective safety enhancement solution

Abstract

In a rapidly flourishing country like Bangladesh, accidents in unmanned level crossings are increasing daily. This study presents a deep learning-based approach for automating level crossing junctions, ensuring maximum safety. Here, we develop a fully automated technique using computer vision on a microcontroller that will reduce and eliminate level-crossing deaths and accidents. A Raspberry Pi microcontroller detects impending trains using computer vision on live video, and the intersection is closed until the incoming train passes unimpeded. Live video activity recognition and object detection algorithms scan the junction 24/7. Self-regulating microcontrollers control the entire process. When persistent unauthorized activity is identified, authorities, such as police and fire brigade, are notified via automated messages and notifications. The microcontroller evaluates live rail-track data, and arrival and departure times to anticipate ETAs, train position, velocity, and track problems to avoid head-on collisions. This proposed scheme reduces level crossing accidents and fatalities at a lower cost than current market solutions. Index Terms: Deep Learning, Microcontroller, Object Detection, Railway Crossing, Raspberry Pi