Automated detection and classification of military warships in overhead imagery

Matthew D. Reisman; Dominic LeDuc; Adam G. Francisco

doi:10.1117/12.2621951

31 May 2022 Automated detection and classification of military warships in overhead imagery

Matthew D. Reisman, Dominic LeDuc, Adam G. Francisco

Proceedings Volume 12096, Automatic Target Recognition XXXII; 120960G (2022) https://doi.org/10.1117/12.2621951
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Matthew D. Reisman, Dominic LeDuc, and Adam G. Francisco "Automated detection and classification of military warships in overhead imagery", Proc. SPIE 12096, Automatic Target Recognition XXXII, 120960G (31 May 2022); https://doi.org/10.1117/12.2621951

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