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.
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, 2022; https://doi.org/10.1117/12.2618325