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Infrared Small Target Detection is a challenging task to separate smalltargets from infrared clutter background. Recently, deep learning paradigmshave achieved promising results. However, these data-driven methods need plentyof manual annotation. Due to the small size of infrared targets, manualannotation consumes more resources and restricts the development of this field.This letter proposed a labor-efficient and cursory annotation framework withlevel set, which obtains a high-quality pseudo mask with only one cursoryclick. A variational level set formulation with an expectation differenceenergy functional is designed, in which the zero level contour is intrinsicallymaintained during the level set evolution. It solves the issue that zero levelcontour disappearing due to small target size and excessive regularization.Experiments on the NUAA-SIRST and IRSTD-1k datasets reveal that our approachachieves superior performance. Code is available athttps://github.com/Li-Haoqing/COM.

Click on Mask: A Labor-efficient Annotation Framework with Level Set for Infrared Small Target Detection