Bag-level classification network for infrared target detection

Connor H. McCurley; Daniel Rodriguez; Chandler Trousdale; Arielle Stevens; Anthony Baldino; Eugene Li; Isabella Perlmutter; Alina Zare

doi:10.1117/12.2618325

31 May 2022 Bag-level classification network for infrared target detection

Connor H. McCurley, Daniel Rodriguez, Chandler Trousdale, Arielle Stevens, Anthony Baldino, Eugene Li, Isabella Perlmutter, Alina Zare

Author Affiliations +

Proceedings Volume 12096, Automatic Target Recognition XXXII; 1209603 (2022) https://doi.org/10.1117/12.2618325
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Aided target detection in infrared data has proven an important area of investigation for both military and civilian applications. While target detection at the object or pixel-level has been explored extensively, existing approaches require precisely-annotated data which is often expensive or difficult to obtain. Leveraging advancements in weakly supervised semantic segmentation, this paper explores the feasibility of learning a pixel-level classification scheme given only image-level label information. Specifically, we investigate the use of class activation maps to inform feature selection for binary, pixel-level classification tasks. Results are given on four infrared aided target recognition datasets of varying difficulty. Results are quantitatively evaluated using common approaches in the literature.

Conference Presentation

Citation Download Citation

Connor H. McCurley, Daniel Rodriguez, Chandler Trousdale, Arielle Stevens, Anthony Baldino, Eugene Li, Isabella Perlmutter, and Alina Zare "Bag-level classification network for infrared target detection", Proc. SPIE 12096, Automatic Target Recognition XXXII, 1209603 (31 May 2022); https://doi.org/10.1117/12.2618325

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