International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Traffic congestion in urban areas poses significant challenges to emergency medical services, where delayed ambulance response times can result in preventable fatalities. Traditional traffic management systems operate on fixed timer-based schedules that fail to adapt dynamically to emergency situations, causing critical delays for ambulances navigating through congested roads. Manual intervention by traffic police is often inefficient and cannot scale across multiple intersections simultaneously. This project presents an innovative artificial intelligence-based solution for automated ambulance detection and emergency traffic clearance using YOLOv5 deep learning architecture. The proposed system leverages state-of-the-art computer vision techniques to detect ambulances in real-time from video feeds captured by traffic cameras or uploaded video files. The system processes visual data through advanced image preprocessing techniques and employs the YOLOv5 object detection algorithm to identify ambulances with confidence scores exceeding 0.5 threshold. The architecture comprises multiple integrated components including camera-based video capture, image preprocessing modules, the YOLOv5 detection engine, traffic signal control interfaces using NTCIP protocol, and comprehensive logging systems. The system supports both real-time video stream processing at 30 frames per second and batch processing of pre-recorded video files. Extensive testing demonstrates detection accuracy exceeding 97% under diverse lighting conditions and traffic scenarios. This cost-effective, scalable solution addresses critical limitations of existing hardware-based traffic management systems by providing a software-centric approach that can be deployed across urban infrastructure with minimal modifications to existing camera networks.

Keywords: Ambulance Detection, YOLOv5, Computer Vision, Traffic Signal Control, Deep Learning, Object Detection, Real-time Processing, Smart City, Emergency Response, Intelligent Transportation Systems

Downloads: | DOI: 10.17148/IJARCCE.2026.151123