Unmanned Aerial Vehicles (UAVs) have experienced remarkable progress and widespread utilization, highlighting the need for robust detection and classification systems to ensure safety and security. This paper presents a comprehensive study on UAV and payload detection and classification, emphasizing the fusion of multiple data sources to enhance accuracy and reliability. A fusion system integrating radar and Pan-Tilt-Zoom (PTZ) camera data is developed and evaluated. Experimental results demonstrate the effectiveness of the proposed approach, achieving a classification accuracy of > 94% for UAV detection and > 90% for payload classification. The findings underscore the system's potential for real-world applications in UAV and payload detection and classification scenarios, addressing the growing demands in this field.


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    Titel :

    Real-Time UAV and Payload Detection and Classification System Using Radar and Camera Sensor Fusion


    Beteiligte:
    Mehta, Varun (Autor:in) / Azad, Hamid (Autor:in) / Dadboud, Fardad (Autor:in) / Bolic, Miodrag (Autor:in) / Mantegh, Iraj (Autor:in)


    Erscheinungsdatum :

    01.10.2023


    Format / Umfang :

    4967868 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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