REAL TIME EMERGENCY VEHICLE DETECTION USING MACHINE LEARNING

Computer Science and Engineering

Emergency vehicle detection in heavy traffic can be challenging due to the presence of multiple vehicles and occlusions. However, we can develop an effective solution to this problem using machine learning and computer vision techniques. Traffic congestion significantly impacts emergency response times, making early detection of emergency vehicles at signals crucial in preventing delays. To address this, the proposed model detects emergency vehicles at a traffic signal and sends real-time alerts to the signal-regulating authorities, enabling the creation of a green corridor for seamless passage. Additionally, when multiple emergency vehicles are stacked at a signal, the system alerts the surrounding traffic police to provide assistance. Once the emergency vehicles are released from the congestion and proceed to another road, a follow-up message is sent, ensuring that unnecessary resources are not deployed in the previous location. This dynamic communication enhances response efficiency and optimizes traffic management. One approach to achieving this involves using the YOLOv5s model to detect and classify emergency vehicles in live video feeds. YOLOv5s is well-suited for real-time object detection tasks and has shown promising results in accurately identifying objects. A diverse dataset containing emergency vehicles in various traffic conditions is used to train the model, ensuring robustness across different environments. Once deployed, the trained model continuously monitors video feeds, detecting emergency vehicles as they approach traffic signals and triggering automated alerts for immediate action. By integrating real-time emergency vehicle detection with intelligent alert messaging, this system enhances emergency response coordination, reduces delays, and optimizes traffic police deployment, contributing to safer and more efficient urban traffic management.

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