Abstract :

A Significant Portion Of Violent Deaths Globally Occur In Traffic Accidents, And Survival Rates Are Impacted By Emergency Response Times. Automated Accident Detection Has Become Essential With The Rise Of Intelligent Traffic Systems And Video Surveillance. This Study Suggests A Deep Learning (DL)-based Technique For Video Traffic Accident Detection. Convolutional And Recurrent Layers Are Used To Extract Visual Features And Detect Temporal Patterns. The Model Shows Efficacy Across Various Road Structures, Achieving 98% Accuracy After Training On Both Public And Custom Datasets. This Method Improves Emergency Response Times, Decreases Reliance On Humans, And Improves Real-time Accident Detection. Keywords: Computer Vision, Deep Learning, Automated Traffic Accident Detection, Visual Features, And Temporal Patterns

Published:

25-9-2025

Issue:

Vol. 25 No. 9 (2025)

Page Nos:

359-366

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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