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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    Title :

    A ResNet-Based Classification Method for Ship Viewpoint Estimation


    Contributors:
    Yang, Xuefeng (author) / Zhou, Changhua (author) / Zhou, Deng (author) / Liu, Jiayao (author)


    Publication date :

    2024-12-27


    Size :

    2302054 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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