Advanced Traffic Incident Detection and Classification with Real-Time Computer Vision

Kolhe Parag Namdeo; Dr. Rajesh Keshavrao Deshmukh

doi:10.32628/IJSRST2411330

Authors

Kolhe Parag Namdeo Research Scholar, Kalinga University, Chhattisgarh, India Author
Dr. Rajesh Keshavrao Deshmukh Professor, Department of Computer science and engineering, Kalinga University, Chhattisgarh, India Author

DOI:

https://doi.org/10.32628/IJSRST2411330

Keywords:

Accident Severity Classification, Postcollision Vehicle Fire Detection, Vehicle Detection, DeepSORT Tracking, Object Detection, Computer Vision

Abstract

To constructively enhance traffic safety measures in Saudi Arabia, a significant number of AI-based traffic surveillance technologies have emerged over the past years, including the widely known Saher system. Rapid detection of vehicle incidents is crucial for improving the response speed of incident management, which in turn minimizes road injuries resulting from accidents. To meet the growing demand for road traffic security and safety, this paper presents a real-time traffic incident detection and alert system based on a computer vision approach.

The proposed framework comprises three models, each integrated within a prototype interface to fully visualize the system’s overall architecture.

Vehicle Detection and Tracking Model: This model uses the YOLOv5 object detector combined with the DeepSORT tracker to detect and track vehicle movements, assigning a unique identification number (ID) to each vehicle. The model achieved a mean average precision (mAP) of 99.2%, ensuring high accuracy in vehicle detection and tracking.
Traffic Accident and Severity Classification Model: Utilizing the YOLOv5 algorithm, this model detects and classifies the severity level of traffic accidents. It attained a mAP of 83.3%. Upon detecting a severe accident, the system sends an immediate alert message to the nearest hospital, ensuring timely medical response.
Fire Detection Model: This model employs the ResNet152 algorithm to detect fire ignition following an accident. It achieved an accuracy rate of 98.9%. If a fire is detected, an automated alert is sent to the fire station, facilitating quick firefighting response.

An innovative parallel computing technique was employed to reduce the overall complexity and inference time of the AI-based system, enabling the proposed system to operate concurrently and in parallel. This parallel processing capability ensures that the detection, classification, and alerting processes occur swiftly and efficiently, enhancing the overall effectiveness of the system.

By integrating these advanced AI models, the real-time traffic incident detection and alert system significantly contributes to improving traffic safety and incident management in Saudi Arabia. The system not only detects and tracks vehicles with high precision but also classifies the severity of accidents and identifies subsequent hazards like fires, ensuring comprehensive and timely responses to traffic incidents. This innovative approach sets a new benchmark for AI-driven traffic safety solutions and offers a scalable model that can be adapted to other regions and contexts.

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