Object detection is a favourite research technology in the field of maritime computer vision and current research mostly adopts region models based on convolutional neural networks (CNNs). Swin-transformer brings a new and effective approach, with its hierarchical structure and attention mechanism-based block, well compensating for the weakness of CNNs that cannot take into account global features. In this paper, we propose a method for applying the Swin-Transformer to maritime object detection, obtaining significantly improved results on the well-known Seaships dataset. Experiments demonstrate that Swin-Transformer, after pre-trained and fine-tuning, has a detection mean average precision of 96.61%, outperforming other CNNs. Furthermore, Swin-Transformer has demonstrated its capability to detect features of the irregular ship shapes in complex backgrounds, and the detection of general cargo ships and passenger ships is excellent.


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    Titel :

    Object Detection in Maritime Scenarios Based on Swin-Transformer


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    S. Shmaliy, Yuriy (Herausgeber:in) / Abdelnaby Zekry, Abdelhalim (Herausgeber:in) / Sun, Wenli (Autor:in) / Gao, Xu (Autor:in)

    Kongress:

    International Conference on Computing, Control and Industrial Engineering ; 2021 ; Hangzhou, China October 16, 2021 - October 17, 2021



    Erscheinungsdatum :

    06.07.2022


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch