Miss Detection vs. False Alarm: Adversarial Learning for Small Object Segmentation in Infrared Images

A key challenge of infrared small object segmentation (ISOS) is to balance miss detection (MD) and false alarm (FA). This usually needs ``opposite'' strategies to suppress the two terms, and has not been well resolved in the literature. In this paper, we propose a deep adversarial learning framework to improve this situation. Departing from the tradition of jointly reducing MD and FA via a single objective, we decompose this difficult task into two sub-tasks handled by two models trained adversarially, with each focusing on reducing either MD or FA. Such a new design brings forth at least three advantages. First, as each model focuses on a relatively simpler sub-task, the overall difficulty of ISOS is somehow…