In this work, we make the first attempt to construct a learning-based single-point annotation paradigm for infrared small target label generation (IRSTLG). Our intuition is that label generation requires just one more point prompt than target detection: IRSTLG can be regarded as an infrared small target detection (IRSTD) task with the target location hint. Based on this insight, we introduce an energy double guided single-point prompt (EDGSP) framework, which adeptly transforms the target detection network into a refined label generation method. Specifically, the proposed EDGSP includes: 1) target energy initialization (TEI) to create a foundational outline for sufficient shape evolution of pseudo label, 2) double prompt embedding (DPE) for rapid localization of interested regions and reinforcement of individual differences to avoid label adhesion, and 3) bounding box-based matching (BBM) to eliminate false alarms. Experimental results show that pseudo labels generated by three baselines equipped with EDGSP achieve 100% object-level probability of detection (Pd) and 0% false-alarm rate (Fa) on SIRST, NUDT-SIRST, and IRSTD-1k datasets, with a pixel-level intersection over union (IoU) improvement of 13.28% over state-of-the-art label generation methods. Additionally, the downstream detection task reveals that our centroid-annotated pseudo labels surpass full labels, even with coarse single-point annotations, it still achieves 99.5% performance of full labeling.

翻译：本研究首次尝试构建基于学习的单点标注范式用于红外小目标标签生成（IRSTLG）。我们的核心观点是：标签生成仅需比目标检测多一个点提示——IRSTLG可视为附带目标位置提示的红外小目标检测（IRSTD）任务。基于此洞见，我们提出能量双引导单点提示（EDGSP）框架，巧妙地将目标检测网络转化为精细的标签生成方法。具体而言，EDGSP包含三个关键模块：1）目标能量初始化（TEI），为伪标签的充分形态演化创建基础轮廓；2）双提示嵌入（DPE），实现感兴趣区域的快速定位并强化个体差异以避免标签粘连；3）基于边界框的匹配（BBM），用于消除虚警。实验结果表明，在SIRST、NUDT-SIRST和IRSTD-1k数据集上，三个基线模型搭载EDGSP生成的伪标签实现了100%的目标级检测概率（Pd）与0%的虚警率（Fa），其像素级交并比（IoU）较当前最先进的标签生成方法提升13.28%。此外，下游检测任务验证显示：基于质心标注的伪标签性能超越全标注标签，即便使用粗糙的单点标注，仍能达到全标注99.5%的性能水平。