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@article{179055,
        author = {Devadarshini S and Deepthi J Mercy and Esha G and Govarthani S and B Bala Abirami},
        title = {Futuristic Roadways Opencv Next Level Vehicle Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5301-5306},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179055},
        abstract = {The growing demand for intelligent transportation systems has emphasized the need for accurate, real-time, and efficient vehicle detection solutions. This paper presents a novel approach that integrates OpenCV and YOLOv8 within a Django-based web framework to enable real-time vehicle detection, classification, and tracking on roadways. Leveraging the power of deep learning and Convolutional Neural Networks (CNNs), particularly the YOLOv8 model, the system ensures high-precision vehicle localization while maintaining computational efficiency. The proposed implementation enhances road safety by providing real-time alerts, aiding in optimized traffic management, and assisting in accident prevention. Additionally, the Django framework offers a scalable and adaptable platform for seamless web-based deployment, facilitating integration with smart city infrastructures. By supporting automated traffic monitoring and intelligent transportation networks, this system contributes to improved urban mobility, reduced congestion, and enhanced roadway efficiency. The experimental results demonstrate the effectiveness of the proposed approach in achieving robust and real-time vehicle detection, making it a valuable tool for modern traffic surveillance and intelligent road monitoring applications.},
        keywords = {Vehicle Detection, YOLOv8, OpenCV, Deep Learning, Traffic Monitoring, Real-Time Tracking, Smart Cities, Computer Vision},
        month = {May},
        }