Image-Based Traffic Accident Detection System Using Deep Learning
Keywords:
Traffic Accident Detection, Deep Learning, Convolutional Neural Networks (CNN, Real-Time Monitoring, Intelligent Transportation SystemsAbstract
Traffic accidents are a major global concern, leading to significant loss of life, injuries, and economic burdens. Timely detection and response to accidents are critical to reducing their severity, yet traditional accident detection methods are often slow, inefficient, and reliant on human intervention. This research proposes an image-based traffic accident detection system using deep learning techniques to address these limitations. Leveraging convolutional neural networks (CNNs), the system analyzes traffic surveillance footage to automatically detect accidents in real-time with high accuracy. The study evaluates the performance of the proposed system in terms of detection accuracy, precision, recall, and real-time processing speed under various environmental conditions, such as changes in lighting, weather, and camera angles. The results demonstrate that the system outperforms traditional sensor-based approaches and is highly adaptable to different traffic environments. Additionally, the study discusses the system’s scalability, cost-effectiveness, and potential impact on improving road safety and traffic management. The findings suggest that the integration of deep learning into traffic monitoring can significantly enhance emergency response times, reduce congestion, and contribute to the development of intelligent transportation systems. However, challenges such as environmental variability and computational resource requirements remain, with future work focusing on further optimization and real-world deployment. This research highlights the potential of deep learning technologies to revolutionize traffic accident detection and improve overall road safety.
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