AI Enabled Accident Detection and Circuit Diagram EmergEye addresses a critical problem in emergency response: delays in detecting and responding to traffic accidents. This issue is especially prevalent in rural areas or less-monitored regions where accidents may go unnoticed for extended periods. Delays in emergency response can lead to increased fatalities and more severe injuries. EmergEye aims to bridge this gap by using real-time video As the number of vehicles increases, road accidents are on the rise every day. According to the World Health Organization (WHO) survey, 1.4 million people have died, and 50 million people have been injured worldwide every year. The key cause of death is the unavailability of medical care at the accident site or the high response time in the rescue operation. A cognitive agent-based collision
This article proposed an intelligent accident detection and rescue system which mimics the cognitive functions of the human mind using the Internet of Things (IoTs) and the Artificial Intelligence This paper presents a real-time road accident detection and alert system that leverages a custom sequential CNN model optimized for rapid accident identification. Unlike existing approaches, this method integrates an automated alert system through Telegram's API, ensuring immediate notification to emergency responders. This enhancement significantly reduces response time, allowing for faster endowed with the capacity to discern subtle spatiotemporal patterns indicative of road accidents. By harnessing the power of deep learning, AcciVue transcends conventional methods, demonstrating unparalleled efficacy in real-time accident detection while laying the foundation for transformative advancements in road safety. B. Data Acquisition
PDF Accident Detection System Using Deep Learning And Edge Computing Circuit Diagram
Title: Car Accident Detection Using Deep Learning with Integrated Emergency Response System. The rapid development of intelligent transportation systems and the rise of smart cities have paved the way for advanced safety mechanisms in vehicular networks.This paper presents a comprehensive approach to car accident detection utilizing deep learning techniques, integrated with OpenCV for image first in the 2020 NVIDIA AI City Challenge anomaly detection track. With a few exceptions that concentrate on unsupervised approaches, the majority of anomaly detection techniques are typically supervised. An unsupervised anomaly detection framework using data gathered from vehicle trajectories was presented by Zhao et al. Abstract: In this study, the research work comes up with a framework that combines IoT with machine learning solutions to improve the real-time detection of vehicle accidents and, at the same time, predict the likelihood of an accident. The framework uses IoT sensors placed on the vehicles to gather a variety of information; speed, acceleration, brake force, and deviation from the lane.
