To improve cutting-edge deep learning techniques for more relevant defense applications, we extend our wellestablished port monitoring ATR techniques from generic ship classes to a pair of newly curated datasets: aircraft carriers and other military ships. We explore several techniques for data augmentation and splits to represent different deployment regimes, such as revisiting known military ports and new observations of never-before-seen ports and ships. We see reliable results (F1 <0.9) detecting and classifying aircraft carriers by type–and by proxy, nationality–as well as encouraging preliminary results (mAP <0.7) detecting and differentiating military ships by sub-class.


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    Titel :

    Automated detection and classification of military warships in overhead imagery


    Beteiligte:

    Kongress:

    Automatic Target Recognition XXXII ; 2022 ; Orlando,Florida,United States


    Erschienen in:

    Proc. SPIE ; 12096


    Erscheinungsdatum :

    31.05.2022





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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