Deep Learning Model for Accident Detection in Smart Cities using Computer Vision

Abstract

In recent years, the concept of smart cities has gained increasing attention, driven by the need to make urban environments safer, more sustainable, and more efficient. One of the key challenges in achieving this vision is the management of traffic, which is a major cause of accidents and delays in urban areas. Traditional methods of accident detection rely on manual reporting or sensor-based systems, which are often slow and imprecise. To address this challenge, researchers have proposed using computer vision techniques to automatically detect accidents in real-time. We have developed a deep learning model; CNN is capable of analyzing real-time video feeds from surveillance cameras installed in strategic locations throughout a city. In this paper, we will describe the proposed model in detail, including the architecture of the CNNs, the training process, and the real-time accident detection algorithm. Experimental observations have shown around 89% of validation accuracy in detecting the accidents based on the live video feed from cameras.