Real time traffic surveillance system using YOLOv9

Urban traffic congestion is a significant challenge that requires creative and prompt solutions to improve traffic management and overall operational efficiency. Conventional traffic management systems frequently depend on static timers, overlooking current traffic situations or the existence of emergency vehicles such as ambulances and fire trucks. There is an urgent need for enhanced vehicle flow detection in developing traffic management systems. This research introduces a smart transportation system that utilizes real-time traffic management, merging conventional and emergency traffic control. Using YOLOv9, the most recent convolutional neural network model, the system monitors the number of vehicles on the road and recognizes emergency vehicles. With this information, the algorithm modifies traffic signals in real time to alleviate congestion and give precedence to emergency vehicles, thereby enhancing response times. Results from experiments show considerable enhancements in travel efficiency and quicker emergency response times, indicating that this method is particularly well-suited for modern traffic management and emergency response systems.