发表机构
Engineering Research Center of Learning-Based Intelligent System, Ministry of Education, Tianjin University of Technology; Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology; School of Computer Science and Engineering, Tianjin University of Technology（教育部基于学习的智能系统工程研究中心，天津理工大学; 教育部计算机视觉与系统重点实验室，天津理工大学; 天津理工大学计算机科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何进行抗噪声框监督红外小目标检测，提出热点锚定标签优化（HALO）方法，通过在框内定位辐射锚合成软标签，将有噪声框监督转换为像素级软监督，实验表明该方法在多种框标注下表现良好且能揭示性能与信杂比关系。
AI 中文摘要
红外小目标检测（IRSTD）通常依赖像素级掩码监督，但此类标注成本高且因红外目标边界模糊和纹理弱而固有不确定。我们将框监督的IRSTD视为不同于通用框到掩码分割和点监督IRSTD的问题，其核心挑战是从高度受污染的框构建稳定的像素级软监督。为此，我们提出热点锚定标签优化（HALO），在局部背景统计约束下在每个框内定位辐射锚，然后围绕锚合成物理锚定高斯（PAG）软标签，将有噪声的框监督转换为连续的像素级软标签。整个过程在训练前离线执行，与检测器主干解耦且无需在线标签更新。在公共数据集上的实验表明，在标准紧密框下HALO与代表性框监督方法具有竞争力，在更宽松或偏移的框标注下更稳健且跨主干保持一致。我们还引入污染感知操作模式分析来表征此类方法的有效边界并揭示固有信杂比与性能的关系。
英文摘要
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.
Comments24 pages, 8 figures
