Vehicle Collision Detection and Alert System

Abstract

In modern vehicles, the collision detection system has become a crucial safety feature, mainly because after the evolution of autonomous and self-driving vehicles. It is proved to be very effective in minimizing the number of road accidents. This proposed system is a working model based system to detect the vehicle collision and give information to the authorities. This project proposes an automated system for vehicle collision detection during high traffic and inform the concerned people using the application. This approach will work on still images, recorded- videos and will detect, classify, track and compute moving object velocity and direction using a convolution neural network. By using YOLO, it will be able to detect the front and rear views of the vehicle and alert us in advance. The advantages of the proposed system are secured, interpretability, high accuracy, lightweight model & fast processing. Moreover, this system can be used in self-driving cars. Where it would analyse the collision possibility automatically and drive accordingly. It could be used in self-driving cars, traffic surveillance systems, traffic management, and automated driving applications.