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Maritime object detection in synthetic aperture radar (SAR) imagery has seen a resurgence of interest due to the introduction of deep object detection models and large-scale datasets from competitions such as xView3. However, as novel examples are seen in the wild and as new SAR sensors emerge, existing models will need to be retrained with relevant new data. Active learning (AL) aims to automate and optimize this data curation process. In this work, we evaluate state-of-the-art AL algorithms for the task of SAR maritime object detection. Through analyzing and identifying gaps in current AL solutions we seek to motivate efforts to improve their utility in practical settings.
Jonathan Sato,Julian Raheema, andMartin Jaszewski
"Evaluating active learning methods for synthetic aperture radar maritime object detection", Proc. SPIE 12276, Artificial Intelligence and Machine Learning in Defense Applications IV, 1227609 (28 October 2022); https://doi.org/10.1117/12.2636037
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Jonathan Sato, Julian Raheema, Martin Jaszewski, "Evaluating active learning methods for synthetic aperture radar maritime object detection," Proc. SPIE 12276, Artificial Intelligence and Machine Learning in Defense Applications IV, 1227609 (28 October 2022); https://doi.org/10.1117/12.2636037