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@article{161854,
        author = {Siva Kumar Nagi and Yeruva MaheswaraReddy},
        title = {Real-time Accident Detection with YOLOv5: Enhancing Public Safety},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {6},
        pages = {390-395},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=161854},
        abstract = {Accident detection and rapid response are critical for ensuring public safety on roadways and in various environments. This study proposes an efficient accident detection system based on the YOLOv5 model, a state-of-the-art object detection framework. The system is designed to identify two major types of accidents: road accidents and fire accidents. By leveraging the capabilities of YOLOv5, our model exhibits high accuracy and real-time performance.
We collected and labeled a dataset of images containing road accidents and fire accidents to train and evaluate the YOLOv5 model. Through extensive experimentation, we achieved robust results in terms of accident detection and localization. The YOLOv5-based system not only provides accurate accident recognition but also offers the potential for rapid response and intervention, enhancing safety measures in various scenarios.
The proposed accident detection system has significant implications for traffic management, surveillance, and public safety, as it can be deployed in real-time monitoring systems, smart cities, and emergency response applications. This research contributes to the field of computer vision and deep learning, demonstrating the practical utility of YOLOv5 in accident detection, which can ultimately save lives and reduce the impact of accidents.
},
        keywords = {Yolov5, accident detection, object detection, YOLO},
        month = {},
        }