Car Accident Detection Using Deep Learning

At present, vehicular collisions constitute the primary contributor to loss of life. The rise in vehicular fatalities has been observed to correspond with the growth in automobile manufacturing. Metropolitan areas also exhibit a heightened mortality rate due to a growing trend of negligence among individuals, resulting in severe consequences. The wide variety of transportation options available in contemporary times is believed to contribute significantly to the increased incidence of automobile accidents. The observed trend indicates a rise in both vehicular and passenger traffic with the growing number of automobiles utilizing the road networks. They present a potential hazard to both physiological and psychological systems. Thus, there exists a need for a dependable and feasible approach to automatic accident detection that leverages methodologies from the domain of image processing. In order to achieve this objective, the employed methodology involves the utilization of image normalization techniques, Convolutional Neural Networks (CNNs), and a Decision Tree algorithm for the purpose of detecting instances of cars accidents.