Emergency Vehicle Object Detection For Traffic Light Optimization

Abstract

In Modern Urban Areas, Traffic Congestion Is A Significant Issue, Especially During Emergencies. Emergency Vehicles Such As Ambulances And Fire Trucks Often Get Delayed Due To Traffic Signals Not Adapting To Their Presence. This Paper Proposes A Deep Learning-based Solution For Real-time Emergency Vehicle Detection And Dynamic Traffic Light Control To Optimize Traffic Flow. Using Object Detection Algorithms Such As YOLO (You Only Look Once), The System Can Accurately Identify Emergency Vehicles From Surveillance Cameras. The Model Then Communicates With A Traffic Control System To Prioritize Signal Changes, Reducing Response Times. This Solution Enhances The Efficiency Of Emergency Services And Can Potentially Save Lives By Minimizing Delays. The Proposed System Integrates Image Processing, Object Detection, And Intelligent Signal Management, Offering A Smart City Innovation For Traffic Optimization Upon Detecting An Emergency Vehicle, The System Dynamically Communicates With A Microcontroller-based Traffic Light System To Switch Signals In Favor Of The Vehicle’s Direction, Creating A "green Corridor." This Ensures That Emergency Vehicles Pass Intersections Without Delay, Minimizing The Need For Manual Intervention.

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