Accident Detection & Smart Traffic System using CNN

The drastic increase in vehicular traffic has led to an upsurge in road accidents. A crucial factor that significantly influences the outcome of an accident is the response time. Delayed response times can lead to fatalities, the main causes being inaccessible medical attention and traffic congestion. In this paper, we propose an effective system to minimize the response time by integrating Accident Detection and Smart Traffic System. Our Accident Detection module uses a Convolutional Neural Network (CNN) for real-time analysis of camera feeds in pre-identified accident-prone areas. Upon detecting an accident, the system promptly sends a notification to the closest hospital via the Telegram app. The notification includes a photo of the accident, date, time and precise location. The Smart Traffic system employs Yolov3 to detect and count vehicles in each lane at a junction. The lane-level microcomputers continuously communicate the vehicle counts to a central unit, which dynamically controls the traffic signals based on the traffic density. Our adaptive approach minimizes the time taken to notify emergency services as well as synchronizes traffic, thereby ensuring faster respite to accident victims.