这篇论文提出了HALO，能用带噪框标注解决红外小目标检测，比现有方法更扛标注不准确，适合实际场景。
该论文针对红外小目标检测中像素级掩码标注成本高且不确定性大的问题，提出HALO（Hotspot-Anchored Label Optimization）方法。HALO在框内定位辐射锚点，并基于局部背景统计合成物理锚定高斯（PAG）软标签，将含噪框监督转化为连续像素级软标签。实验在公共数据集上显示，标准紧框下HALO与代表性盒监督方法竞争力相当，而在更宽松或偏移框条件下（更贴近实际场景），HALO鲁棒性显著提升，且不同骨干网络下表现一致。论文还引入污染感知工作区间分析，揭示内在信杂比与性能的关系。
Noise-Robust Box-Supervised Infrared Small Target Detection via Physics-Inspired Soft Label Optimization
Infrared small target detection (IRSTD) commonly relies on pixel-level mask supervision. Such annotations, however, are costly and inherently uncertain because infrared targets have blurred boundaries and weak textures. We formulate box-supervised IRSTD as a problem distinct from generic box-to-mask segmentation and point-supervised IRSTD. Its central challenge is to construct stable pixel-level soft supervision from highly contaminated boxes. To this end, we propose Hotspot-Anchored Label Optimization (HALO). HALO localizes a radiometric anchor inside each box under local background-statistics constraints, then synthesizes a Physically Anchored Gaussian (PAG) soft label around the anchor. This turns noisy box supervision into continuous, pixel-level soft labels. The entire process is performed offline before training, remains decoupled from the detector backbone, and requires no online label updates. Experiments on public datasets show that HALO is competitive with representative box-supervised methods under standard tight boxes. Under looser or shifted box annotations that better approximate real scenarios, HALO is substantially more robust while remaining consistent across backbones. We further introduce a contamination-aware operating-regime analysis to characterize the effective boundary of this class of methods and reveal how intrinsic signal-to-clutter ratio relates to performance.