The application scope of UAV in the power field is gradually expanding, which puts forward higher requirements for the accurate landing of UAV. At this stage, the fixed-point landing of UAV mainly depends on RTK, but RTK is easy to be disturbed by the external environment, so it can not meet the requirements of fine landing. This paper proposes an improved yolov4-tiny algorithm, which adds the convolutional attention module to the yolov4-tiny network structure feature pyramid to reduce the interference of complex backgrounds on target recognition. In this system, the UAV is guided to fly above the nest by RTK in the first, and then the landing code is recognized by improved yolov4 tiny algorithm, so as to realize the accurate landing of UAV. Through the field fine landing test of UAV, it is found that the landing accuracy of the proposed technology is high and meets the landing requirements.


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    Titel :

    Research on a UAV fine descent system based on improved YOLOv4-tiny


    Beteiligte:
    Liu, Tianli (Autor:in) / Wang, Tao (Autor:in) / Li, Chunfei (Autor:in) / Liu, Yue (Autor:in) / Zhou, Changming (Autor:in) / Wu, Jian (Autor:in)

    Kongress:

    International Conference on Mechanisms and Robotics (ICMAR 2022) ; 2022 ; Zhuhai,China


    Erschienen in:

    Proc. SPIE ; 12331


    Erscheinungsdatum :

    10.11.2022





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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