AUTOMATED ACCIDENT DETECTION USING DEEP LEARNING | IJCT Volume 13 – Issue 2 | IJCT-V13I2P36
International Journal of Computer Techniques
ISSN 2394-2231
Author
G.Ramkumar, CH.Vasanth, A . Gnaneswar, Dr.K.Akila
Abstract
Road accidents are a major cause of injuries and loss of life worldwide. Timely detection of accidents is critical to ensure quick emergency response and reduce fatalities. This project proposes an Automated Accident Detection System using Deep Learning to identify road accidents in real time from traffic video footage. The system analyzes video streams captured from CCTV cameras and detects accident-like events by learning vehicle motion patterns, collisions, and sudden changes in movement. A deep learning–based model is trained using both accident and non-accident video data to accurately classify events. The proposed system reduces the need for manual monitoring and eliminates the requirement for additional hardware. Experimental results demonstrate that the system achieves reliable detection accuracy and can operate effectively under various traffic and lighting conditions, making it suitable for smart transportation and intelligent surveillance applications.
Keywords
AD, Natural language Processing, Clinical text summarization, Medical Decision support, Automated reporting.
Conclusion
AI-driven Automated Accident Detection (AD) systems play a crucial role in improving road safety, traffic monitoring efficiency, and emergency response in modern transportation environments. By continuously analyzing large volumes of traffic video data, these systems identify accident events in real time and support rapid decision-making through the use of deep learning, computer vision, and intelligent data-analysis techniques.
1.By leveraging deep learning and historical traffic data, AD systems can anticipate high-risk situations and potential accident scenarios, allowing proactive intervention and improved traffic management.
2.Advanced accident detection systems support autonomous decision-making by automatically identifying accidents, estimating severity, and triggering alerts without requiring continuous human supervision.
3.The integration of information from multiple cameras, sensors, and traffic databases into a centralized detection platform enhances situational awareness and provides a comprehensive view of road conditions.
4.Early accident detection and rapid alert generation significantly reduce emergency response time, thereby minimizing injuries, preventing secondary accidents, and improving overall road safety. Timely detection and automated incident management reduce manual monitoring costs, operational overhead, and losses associated with delayed emergency response.
Automated Accident Detection (AD) systems based on deep learning and artificial intelligence represent a major advancement in intelligent transportation and road safety management. By leveraging continuous video surveillance, real-time data processing,
References
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How to Cite This Paper
G.Ramkumar, CH.Vasanth, A . Gnaneswar, Dr.K.Akila (2026). AUTOMATED ACCIDENT DETECTION USING DEEP LEARNING. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
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