AI-Based Road Accident Detection Using Deep Learning

Abstract

Road accidents are one of the major causes of injuries and fatalities worldwide. Early detection of accidents can significantly reduce response time and emergency services reach the location quickly. This paper proposes an AI-based road accident detection system using deep learning and computer vision techniques. The system analyzes traffic video footage to detect vehicles and identify possible accident events. The YOLOv8 object detection model is used to detect vehicles in video frames, while the Deep SORT tracking algorithm tracks vehicle movements across consecutive frames. By analyzing abnormal motion patterns such as collisions or sudden stops, the system identifies potential accident events. When an accident is detected, the system captures the accident frame and generates an alert notification. This approach can help improve road safety by enabling faster accident detection and response.

Introduction

The text presents an AI-based road accident detection system designed to improve traffic monitoring and reduce delays in emergency response. Traditional methods rely on manual observation or witness reports, which are often slow and inefficient.

The proposed system uses deep learning and computer vision to automatically detect accidents from video footage. It combines:

• YOLOv8 for real-time vehicle detection, and
• Deep SORT for tracking vehicle movements across frames.

The system processes video input (from dashcams or surveillance cameras), splits it into frames, and analyzes vehicle motion patterns. By identifying abnormal behaviors—such as sudden stops or collisions—it can detect accidents, capture relevant frames, and generate alerts for authorities.

The architecture includes four main modules:

1. Video input and frame processing
2. Vehicle detection (YOLOv8)
3. Vehicle tracking (Deep SORT)
4. Accident detection and alert generation

Results show that the system effectively detects vehicles and tracks their movement, enabling accurate identification of accident events. The combination of detection and tracking improves reliability and performance compared to traditional systems.

Conclusion

This paper presented an AI-based road accident detection system using deep learning and computer vision techniques. The system uses the YOLOv8 model for vehicle detection and the Deep SORT algorithm for tracking vehicle movement across frames. By analyzing vehicle interactions and motion patterns in traffic videos, the proposed system can detect abnormal events that may indicate accidents. Once an accident is detected, the system captures the accident frame and generates alerts to notify authorities. The proposed system can help improve road safety by enabling faster accident detection and response. In future work, the system can be enhanced by integrating real-time traffic camera feeds and improving detection accuracy using larger datasets and advanced deep learning models. In future work, the proposed system can be improved by integrating real-time traffic camera feeds and advanced deep learning models to enhance detection accuracy. The system can also be extended to automatically notify emergency services and provide location information to ensure faster response to accident events.

References

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Copyright

Copyright © 2026 Lana Rose H, Subha Darathy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.