Emergency Vehicle Detection Using Yolov9 & RCNN

Abstract

The Emergency Vehicle Detection System is a real-time intelligent solution designed to identify ambulances, fire trucks, and other emergency vehicles swiftly and accurately. By incorporating cutting-edge machine learning models such as YOLOv9 and Faster R-CNN, the system ensures high-speed object detection and precise classification in complex urban traffic conditions. Detection begins by analyzing multi-source video feeds like CCTV, webcams, and RTSP streams, enhancing the system’s reliability across various scenarios. It uses a trained dataset with labeled Using data augmentation techniques for both general traffic and emergency vehicles to improve performance in challenging environments. The fusion of YOLOv9's speed and R-CNN’s accuracy results in balanced and efficient detection. This approach reduces delays in emergency response by enabling dynamic traffic management and vehicle prioritization. Evaluation measures like recall, accuracy, and precision attest to the system’s effectiveness. In the future, the model can expand to include other priority vehicles and sensor integration for smarter urban mobility. Overall, the system offers a fast, accurate, and reliable detection framework for improving public safety.