Creating a real-time detection model for flying objects, Unmanned Aerial Vehicle (UAV), addressing the growing need for effective surveillance against drones and other airborne threats. By training on a diverse dataset with multiple classes of flying objects, the model is designed to recognize a variety of shapes and sizes, making it suitable for challenging environments where objects may be small, occluded, or blended into the background. Using the YOLOv8 architecture, the model aims to deliver both high detection accuracy and fast processing speeds, ensuring it is both reliable and adaptable for real-world security and monitoring applications.


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    Titel :

    Real-Time Detection of Unmanned Aerial Vehicles Using YOLOv8


    Beteiligte:
    V, Pavan Kumar (Autor:in) / Reddy, G. Likhith (Autor:in) / Mahadev, H. (Autor:in) / Harish Kumar, V. (Autor:in) / Prasad, K. Krishna (Autor:in)


    Erscheinungsdatum :

    25.06.2025


    Format / Umfang :

    1248476 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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