Advanced Detection of Emergency Vehicles through Multi-Source Data with YOLOv9 & R-CNN

In the dynamic realm of modern traffic system there must be a improved road safety and automating traffic management systems required in the essential duty of emergency vehicle detection.The quick and precise identification of emergency vehicles, such as fire vehicles and ambulances, is crucial.By making sure that emergency vehicles are safe and easily identifiable, which facilitates quick emergency response.In order to distinguish emergency vehicles from various domestic vehicles by having data accessible from multiple sources(CCTV,Webcams),This work is carried out in YOLOV9(YOU ONLY LOOK ONCE).The sophisticated problem of identifying emergency vehicles among the clutter of bustling traffic and a variety of environmental circumstances is taken on by YOLOV9, It is highly regarded for its accuracy and real-time processing.The traffic data is utilized to predict the model after being extracted from various sources, including CCTV footage, photos, videos, and webcams.Our project design places a high priority on the capacity to accurately detect emergency vehicles in bustling traffic.