Ship viewpoint estimation enhances perception capabilities, overcoming radar and AIS limitations in motion parameter acquisition. A dataset with diverse ship types and viewpoints was constructed using Unity3D simulation. The proposed method discretizes viewpoint estimation into 360 categories using ResNet, with localization cropping, image pyramids, and a probability-weighted calculation to reduce ambiguity and improve stability. Experiments showed an average error of 1.61° and a 7% accuracy improvement over the baseline, highlighting its practical potential.


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    Titel :

    A ResNet-Based Classification Method for Ship Viewpoint Estimation


    Beteiligte:
    Yang, Xuefeng (Autor:in) / Zhou, Changhua (Autor:in) / Zhou, Deng (Autor:in) / Liu, Jiayao (Autor:in)


    Erscheinungsdatum :

    27.12.2024


    Format / Umfang :

    2302054 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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