ACCIDENT DETECTION USING TENSOR FLOW AND CNN

Abstract

Accident detection is a critical aspect of road safety management, with timely identification and response being paramount to minimizing casualties and damage. In this project, a comprehensive approach to accident detection utilizing Deep learning techniques is proposed. The system aims to automatically detect accidents in real time using information obtained from diverse sources like video feeds, sensor inputs, and audio signals. The project involves the development and implementation of Deep learning models trained on labeled datasets containing examples of accident and non-accident scenarios. Supervised learning techniques like classification, regression and advanced neural network architecture like convolution, recurrent neural network are considered to effectively process and analyze the data. The deployed system continuously monitors the environment, processing incoming data streams to identify patterns indicative of accidents. Upon detection, the system triggers appropriate response mechanisms, such as alerting emergency services or nearby vehicles, facilitating swift intervention. The project aims to contribute to road safety initiatives by providing a scalable and efficient solution for accident detection. Additionally, the insights gained from the data collected by the system can inform policymakers and urban planners in implementing measures to prevent accidents and improve overall road safety. Through this project, the students gain valuable experience in applying Deep learning techniques to address real-world challenges and contribute to societal welfare.