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
this https URL

这篇论文提出了一种劳动效率高且粗略标注框架，通过使用级数集来获得仅需一次粗略点击即可得到高质量伪掩码，解决了因小目标尺寸和过度正则化导致的零水平轮廓消失的问题，实验表明该方法在 NUAA-SIRST 和 IRSTD-1k 数据集上具有优异的性能。