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@article{169922,
        author = {Mr. Ganesh Bokare and Miss. Pallavi Raut and Miss. Shivani Thakare and Miss. Snehal Timande and Prof. A.S. Kamble},
        title = {A Review on YOLO and CNN Powered real time Accident Detection System for road safety},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2789-2794},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169922},
        abstract = {Road accidents remain a significant source of human suffering and economic loss worldwide. Traditional methods of accident detection often rely on manual reporting, resulting in delayed emergency response and potentially preventable fatalities. This thesis presents an automated Accident Detection System utilizing YOLOv8 and Convolutional Neural Networks (CNNs) to enhance real-time accident recognition and reporting. YOLOv8, known for its high-speed and accurate object detection, serves as the primary tool for identifying potential accident events. Meanwhile, CNNs provide additional analytical power, identifying complex patterns within accident scenes to improve detection accuracy. By significantly reducing response times, this system aims to improve emergency response efficiency, thereby contributing to saving lives and minimizing accident-related losses. This study demonstrates the potential of advanced deep learning algorithms to transform accident detection, providing a reliable alternative to traditional methods.},
        keywords = {Road accidents, Accident detection, YOLOv8, Convolutional Neural Networks (CNNs), Real-time detection, Emergency response, Object detection.},
        month = {November},
        }