Intelligent Traffic Accident Detection Using Computer Vision

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

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

Computer Vision Simulation for Traffic Violation Detection

Traffic safety remains a critical concern in modern urban mobility, and the violation of red traffic signals is among the most dangerous and frequent forms of non-compliance, contributing substantially to intersection collisions, injuries, and fatalities. Conventional monitoring, which relies on human officers and fixed cameras reviewed manually, is labour-intensive, error-prone, and unable to provide continuous, scalable coverage across the many intersections of a growing city. This study proposes and demonstrates a computer-vision framework for the automatic detection of red-light running, formalised through the logical rule Violation = RedLight ? VehicleCrossesStopLine. The system integrates traffic-light state recognition, vehicle detection through bounding boxes, and stop-line region-of-interest analysis within a sequential processing pipeline comprising frame extraction, preprocessing, detection, and decision modules. A browser-based prototype built with HTML5 Canvas and JavaScript was developed to embody the complete detection logic, enabling red, yellow, and green signal states, a defined stop-line region, and moving vehicles to be evaluated in real time. The system was assessed on 200 simulated events spanning four representative scenarios using accuracy, precision, recall, and F1-score. Experimental results yielded an accuracy of 94.5%, precision of 95.2%, recall of 93.1%, and an F1-score of 94.1%, confirming reliable discrimination between violating and compliant vehicles. The principal contribution is a lightweight, transparent, and extensible detection scheme that provides a foundation for intelligent transportation systems, supporting future integration with CCTV networks, YOLOv8 detectors, web dashboards, and electronic ticketing.

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.

Efficient Traffic Rule Enforcement Framework using YOLOv8-Based Object Detection

With the increasing number of vehicles, urbanization and the constant rise in road usage, traffic violations have become hugely problematic in today's transportation situations. Some of the most common dangerous driving behaviors that lead to road accidents and traffic delays are as follows: Not wearing a helmet, running a red light, and breaking lanes, breaking seatbelt, using a cell phone while driving and triple riding. Maintaining consistent observation, precise detection, scalability, and speedy identification of traffic infractions in complex road situations are all challenges faced by current traffic violation monitoring methods. Factors such as high traffic levels, uneven lighting, environmental interference, and requiring human supervision limit the effectiveness of current monitoring methods. Therefore, it becomes essential to have a sophisticated automated system that can efficiently do real-time traffic infraction analysis. The proposed study utilizes a novel Traffic violations identification method based on YOLOv8 to detect many traffic violations in the surveillance photos and videos. To achieve the system execution, a Traffic Rule-net Dataset was developed from a set of traffic data collected from different scenarios of city transport, highways and crossroads. The quality of the features and the robustness of the suggested model were enhanced using a number of data pre-processing techniques, including normalization, image size modification, data augmentation, and filtering. The new framework was applicable in all environmental conditions, allowing for efficient object localization and classification of different breaches. From the experimental evaluation it is clear that there was a reduction in false detection, performance, and detection efficiency. Infrancements of the traffic rules may be easily and efficiently detected for traffic control through the use of intelligent monitoring.

RoadAware: Real-Time Hazard Detection and Driver Awareness System for Mobile Devices

An enhanced road safety system that combines motion sensor-based detection with computer vision algorithms to create a more comprehensive hazard alert system and demonstrates significant improvements in detection accuracy.