Real-Time Traffic Accident Detection Using an Enhanced Deep Learning Ensemble Model

Road Accident Detection and Alert System using Deep Learning and Computer Vision

A Smart City Framework for Real-Time Traffic Monitoring and Accident Detection

Smart city transport networks must be highly adaptive, meaning they can quickly adjust to new road conditions. This research aims to provide a smart city architecture that can detect accidents and track traffic in realtime using edge-cloud computing, deep learning-based video analytics, and IoT sensing. In order to correctly analyse traffic and detect accidents, the platform continuously gathers heterogeneous data from roadside cameras and automobile sensors, performs essential analytics at the edge to decrease latency, and runs robust cloud analytics. The software is able to do precise traffic analyses and detect accidents because of this. Abnormal traffic event spatial and temporal patterns are captured using a mixed deep learning architecture employing recurrent neural networks and convolutional neural networks. Also, for proactive traffic management, a module that forecasts traffic patterns can be used. The proposed system exhibits low response time, robustness under varying traffic and lighting conditions, and outstanding detection accuracy, according to the experimental results. The system is both scalable and inexpensive, and it improves urban mobility, response times to emergencies, and road safety.

Smart Road Traffic Monitoring: Unveiling the Synergy of IoT and AI for Enhanced Urban Mobility

Anomaly detection has become an important area of research because of its relevance across a wide range of fields, including surveillance, transportation, healthcare, and public safety. With the rapid expansion of urban environments, the need for effective monitoring systems has increased significantly. Modern cities depend heavily on surveillance infrastructure, particularly CCTV cameras, to observe traffic conditions on roads, highways, and public intersections. While these systems generate a continuous stream of visual data, relying on human operators to monitor them is both impractical and inefficient. Continuous observation can lead to fatigue, reduced attention, and delayed responses, especially when dealing with large-scale surveillance networks. These limitations highlight the necessity for automated systems capable of identifying unusual events accurately and in real time. In this context, the present study focuses on the detection of road accidents using deep learning techniques applied to surveillance video data. Road accidents remain a major global issue, contributing to loss of life, physical injuries, traffic disruption, and economic costs. A critical factor in reducing the impact of such incidents is the speed at which they are detected and reported. Delays in identifying accidents often result in slower emergency response times, which can worsen outcomes. Therefore, there is a clear need for intelligent systems that can recognize accident scenarios as they occur and promptly alert the relevant authorities.

Artificial Intelligence Models for Smart Traffic Management and Road Safety

An overview of the most prominent types of AI models employed in smart traffic control and road safety, such as supervised and unsupervised machine learning, deep learning, computer vision, and reinforcement learning, and their applications for traffic flow prediction, adaptive traffic signal control, vehicle and pedestrian detection, accident prediction, driver behaviour monitoring, and prioritisation of emergency vehicles are discussed.

An efficient computer vision framework for RSU-based accident recognition and V2X communication

Road traffic accidents require rapid detection and timely warning dissemination to reduce secondary collisions and improve emergency response. This paper presents an efficient roadside-unit-based computer vision framework for real-time accident recognition integrated with Vehicle-to-Everything communication. The proposed architecture combines lightweight convolutional spatial feature extraction, optical-flow-based motion analysis, temporal score aggregation, and low-latency V2X alert generation within an edge-deployable RSU pipeline. A formal system model is introduced to describe accident likelihood estimation, detection decision making, latency constraints, and communication reliability requirements. The experimental evaluation was conducted on a traffic video dataset containing 3,300 clips with normal traffic, single-vehicle accidents, multi-vehicle collisions, and non-accident anomalies. The proposed hybrid framework achieved 95.8 % accuracy, 95.0 % precision, 94.6 % recall, and 94.8 % F1-score, outperforming CNN-only and motion-only baselines. The average processing latency was approximately 40 ms per frame, indicating the feasibility of real-time operation on RSU-grade embedded hardware. These results show that the proposed framework can provide accurate and timely accident detection while supporting rapid V2X warning dissemination for next-generation intelligent transportation systems.

AI-Powered Road Accident Detection Using Learning

Road accidents are a major cause of injuries, fatalities, and traffic disruptions worldwide. Timely detection of vehicle accidents is critical for providing quick emergency response and reducing the impact of such incidents. This project presents an AI-powered real-time Vehicle Accident Detection system developed using Python, OpenCV, and Deep Learning techniques. The system analyzes live video streams or recorded footage to automatically detect and classify vehicle accidents.The proposed approach uses computer vision methods through OpenCV to process video frames and extract meaningful visual information. A Convolutional Neural Network (CNN) is trained on image and video datasets containing accident and non-accident scenarios. The CNN model learns spatial features such as vehicle movement, collision patterns, and sudden changes in motion to accurately identify accident events. Once an accident is detected, the system can generate automatic alerts to notify concerned authorities or emergency services.This project demonstrates an end-to-end implementation, starting from dataset preparation and model training to real-time deployment. The system improves accident detection accuracy compared to traditional methods and reduces dependency on manual monitoring. Overall, this AI-based accident detection system highlights the effective use of deep learning and computer vision technologies to enhance road safety and support faster emergency response mechanisms