Using unmanned aerial vehicles (UAVs) to take aerial images and detect ships in harbors provides a new way of harbor monitoring. However, due to the limitation of computing resources and storage resources, ship detection on UAVs is very challenging, which puts a higher demand on model lightweighting. In this paper, we propose an efficient model lightweighting scheme based on knowledge distillation. We use two advanced large-scale models YOLOv7 and PP-YOLO as teacher models, and transfer the excellent detection ability of these two models to small-scale student models YOLOv7-tiny through knowledge distillation. This scheme not only greatly reduces the parameter scale and computation, but also retains the ship detection performance equivalent to that of large models.


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    Titel :

    Lightweight in-harbor ship detection based on UAV aerial images


    Beteiligte:
    Zuo, Chao (Herausgeber:in) / Zhang, Chunlei (Autor:in) / Nie, Jing (Autor:in)

    Kongress:

    International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023) ; 2023 ; Changsha, China


    Erschienen in:

    Proc. SPIE ; 12710 ; 127100U


    Erscheinungsdatum :

    01.06.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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