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@article{177950,
        author = {PRABHAKAR ELE and P. AJAY KUMAR REDDY and K. KALYAN KUMAR and M. VAMSHI NAIK},
        title = {AI-BASED ACCIDENT DETECTION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2104-2109},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177950},
        abstract = {Road accidents have become a serious concern in today’s world, often leading to injuries, loss of life, and delayed emergency response. To help address this issue, this project presents an AI-based accident detection system that uses a camera and deep learning techniques to automatically identify accidents as they happen. The system is built using a Raspberry Pi 4 and a camera module to capture live video. A trained object detection model is used to analyze the video and detect accidents. When an accident is detected, the system sends an alert message instantly using Twilio SMS service, helping to ensure a quicker response from emergency services or concerned authorities. While the system works best in controlled setups, it represents an early step toward developing low-cost, intelligent solutions for accident detection. With further improvements, it could potentially be applied in scenarios such as accident-prone zones and remote highway stretches.},
        keywords = {YOLOv5n, Accident Detection, ONNX, Raspberry Pi 4, Flask, Twilio},
        month = {May},
        }