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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    Title :

    Performance Analysis of YOLOv7 and YOLOv8 Models for Drone Detection


    Contributors:
    Agarwal, Khushi (author) / M S, Ashik Sanyo (author) / Bakshi, Srishti (author) / M, Vinay (author) / J, Jayapriya (author) / S, Deepa (author)


    Publication date :

    2023-09-01


    Size :

    937316 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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