Click on Mask: A Labor-efficient Annotation Framework with Level Set for
  Infrared Small Target Detection
Haoqing Li Jinfu Yang Yifei Xu Runshi Wang
Infrared Small Target Detection is a challenging task to separate small
targets from infrared clutter background. Recently, deep learning paradigms
have achieved promising results. However, these data-driven methods need plenty
of manual annotation. Due to the small size of infrared targets, manual
annotation consumes more resources and restricts the development of this field.
This letter proposed a labor-efficient and cursory annotation framework with
level set, which obtains a high-quality pseudo mask with only one cursory
click. A variational level set formulation with an expectation difference
energy functional is designed, in which the zero level contour is intrinsically
maintained during the level set evolution. It solves the issue that zero level
contour disappearing due to small target size and excessive regularization.
Experiments on the NUAA-SIRST and IRSTD-1k datasets reveal that our approach
achieves superior performance. Code is available at
https://github.com/Li-Haoqing/COM.