Efficient fine-grained automatic target recognition through active learning for defense applications

• Main Authors: Thorp, Claire A., Sisti, Sean P., Browne, Lesrene A., Schwartz, Casey, Inkawhich, Nathan, Bennette, Walter
• Format: Conference Proceeding
• Language: English
• Published: SPIE 07.06.2024
• ISBN: 1510674209; 9781510674202
• ISSN: 0277-786X
• DOI: 10.1117/12.3013500

Out of distribution (OOD) detection has shown immense promise to enable Automatic Target Recognition models for defense applications. However, many defense applications have constraints that make current best practices for training OOD detection models challenging. These include: the need to perform fine-grained classification of identified targets, low amounts of labeled data to train models, limited availability of Subject Matter Experts to accurately label new data, and the potential need to incorporate new classes of targets as they are discovered. Given these constraints, we propose to build a fine-grained classifier with robustness against OOD data through an active learning approach - designed to further classify objects after detection through some coarse-grained object detection model. This paper will explore active learning methods for Automatic Target Recognition applications, with experiments conducted using the fine-grained overhead imagery dataset, ShipsRSImageNet, along with samples from the DOTA dataset as an exposure set. Our contributions will include recommendations to achieve fine-grained Automatic Target Recognition with robustness against OOD data with minimal labeling from Subject Matter Experts.