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@article{194917,
        author = {D. Rishitha and Soha Jabeen and J. Greeshma Reddy},
        title = {CNN-Based Real-Time Accident Detection and Emergency Response System Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {5953-5960},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194917},
        abstract = {Road accidents are one of the main reasons for fatalities and serious injuries. Early detection and response are critical in minimizing the damage and saving lives. In this paper, a CNN-based real-time accident identification and emergency notification system based on computer vision methods. The proposed system applies computer vision methods to analyze CCTV traffic video footage and employs a YOLO-based CNN model to identify accident events. The model is trained with accident-related data and can identify accidents instantly. When an accident occurs, the system instantly sends a notification and forwards location details to emergency responders. The system also provides a monitoring dashboard for visualization and management of accidents. The Experimental evaluation shows that the model achieves strong detection accuracy and helps reduce emergency response delays. The system can be integrated into smart city infrastructures for enhancing road safety and traffic monitoring.},
        keywords = {Traffic Accident Detection, Deep Learning Methods, CNN, YOLO, Vision-based analysis, Emergency Response System, Smart Transportation.},
        month = {March},
        }