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.

Links:

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