Nowadays, allowing unmanned aerial vehicles (UAVs) to accompany humans in daily life has become a hot topic. Pedestrian detection plays an important role on this application with its accuracy and real-time detection. In this paper, we design an on-board real-time pedestrian detection method for micro UAVs based on YOLO-v8 network. More specifically, several custom network models of small scales are fine-tuned based on YOLO-v8 to detect pedestrians in real-time. Besides, our detection methods are implemented and deployed on a custom micro UAV. Through simulation and real-world experiments, the results show that the custom detection network models based on YOLO-v8 can accurately detect pedestrians at up to 34 FPS on Jetson Xavier NX.


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    Titel :

    On-Board Real-Time Pedestrian Detection for Micro Unmanned Aerial Vehicles Based on YOLO-v8


    Beteiligte:
    Li, Zhiwei (Autor:in) / Liu, Zhihong (Autor:in) / Wang, Xiangke (Autor:in)


    Erscheinungsdatum :

    2023-07-25


    Format / Umfang :

    1082664 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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