Drone detection techniques are used to detect unmanned aerial systems (UAS) also commonly known as drones. A rapid increase in these drones has limited the airspace safety and so the research for drone detection has emerged. This study compares between the two widely used deep-learning models, previously used YOLOv7 and the latest YOLOv8. The overall finding of this study suggests that the YOLOv8 deep-learning model appears to be more promising and may make valuable contributions on their own. We got the result that for 10 epochs YOLOv8 gave 50.16% accuracy while YOLOv7 gave 48.16% accuracy making YOLOv8 more promising for the task. As a practical application for future work, we intend to deploy YOLOv8 on edge devices to achieve real-time drone detection in critical security applications.


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    Titel :

    Performance Analysis of YOLOv7 and YOLOv8 Models for Drone Detection


    Beteiligte:
    Agarwal, Khushi (Autor:in) / M S, Ashik Sanyo (Autor:in) / Bakshi, Srishti (Autor:in) / M, Vinay (Autor:in) / J, Jayapriya (Autor:in) / S, Deepa (Autor:in)


    Erscheinungsdatum :

    01.09.2023


    Format / Umfang :

    937316 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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