CNN-Based Accident Detection and Emergency Response System

Tanmay Chavan , Harsh Gharat , Sumed Ingale , Divanshu Sharma , Prof. Anuja Chandane

Accident Detection; Convolutional Neural Networks; Emergency Response; YOLOv8

Road accidents remain a major global challenge, requiring rapid and effective emergency response mechanisms. Traditional accident detection methods rely on manual reports or eyewitness accounts, resulting in delays in response times and increased severity of the consequences. This paper presents a CNN-based accident detection and emergency response system using YOLOv8, an advanced deep learning algorithm for real-time video analysis. The system processes live video surveillance images to detect accidents quickly, records critical details such as location, time, and severity, and triggers immediate alerts to the emergency services. The proposed system automates accident monitoring, integrating machine learning and computer vision technologies to achieve accuracy while minimizing false positives and negatives. It features a user-friendly interface for traffic authorities and emergency responders, a robust detection module powered by convolutional neural networks, and a scalable database for efficient storage and analysis of accident data. The methodology includes data collection, pre-processing, model training, evaluation, and deployment, guaranteeing the adaptability of the system, deployment, and ensuring adaptability in a variety of environments, including urban areas, freeways, and high-traffic areas. The preliminary results from controlled experiments demonstrate the system's potential to significantly reduce emergency response times and coordination between emergency services. By filling the gaps left by traditional detection systems, this study provides a scalable and reliable solution that contributes to intelligent transportation systems and improves public safety through real-time monitoring notifications and effective emergency management.

Volume 14 Issue 2, February-2026

Pages : 17-33