发表机构
School of Computer Science and Technology, Beijing Institute of Technology; School of Information and Electronics, Beijing Institute of Technology（北京理工大学计算机科学与技术学院; 北京理工大学信息与电子学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对红外小目标检测中现有方法在尺度和背景变化时性能不稳定的问题，本文提出RDANet，通过MSAD和PGSM模块提升检测鲁棒性，在三个公共基准上取得最优性能且表现更稳定。
AI 中文摘要
红外小目标检测在遥感图像中仍然具有挑战性，因为目标极小、局部对比度弱，且常嵌入复杂多变的背景中。除这些固有困难外，研究发现现有检测器在目标尺度变化或场景背景变化时性能不稳定。这种对尺度和场景敏感的退化现象表明，当前方法在特征下采样时无法同时保留目标结构，且在背景变化下无法维持判别性局部对比度，最终导致不同条件下检测性能不平衡。为提升检测鲁棒性，本文提出用于红外小目标检测的相对退化感知网络（RDANet）。RDANet包含两个专用模块：多尺度抗锯齿下采样（MSAD）和原型引导跳跃记忆（PGSM）。MSAD引入多尺度抗锯齿滤波与像素折叠聚合，以降低分辨率降低过程中的混叠效应，从而更好保留目标形状信息，同时抑制无关背景响应。PGSM通过从共享记忆中检索 patch 级原型并自适应将其整合到当前表示中，进一步增强跳跃特征，有助于在多样化场景背景下维持稳定的局部对比度线索。在三个公共基准上的实验表明，RDANet在大多数评估指标上取得最佳性能，而按尺度和背景分层的评估显示其在不同目标尺寸和场景复杂度下表现更稳定。代码可在指定 URL 获取。
英文摘要
Infrared small target detection is still challenging in remote sensing imagery, because the targets are extremely small, exhibit weak local contrast, and are often embedded in complex and highly variable backgrounds. In addition to these inherent difficulties, we observe that existing detectors often show unstable performance when the target scale changes or when the scene background varies. This scale- and scene-sensitive degradation indicates that current methods are insufficient in simultaneously preserving target structure during feature downsampling and maintaining discriminative local contrast under background shifts, which finally results in unbalanced detection performance across different conditions. To improve detection robustness, this paper proposes a Relative Degradation Aware Network (RDANet) for infrared small target detection. RDANet consists of two dedicated modules: Multi-Scale Anti-Alias Downsampling (MSAD) and Prototype-Guided Skip Memory (PGSM). MSAD introduces multi-scale anti-alias filtering together with pixel-fold aggregation to reduce aliasing effects during resolution reduction, so that target shape information can be better preserved while irrelevant background responses are suppressed. PGSM further enhances the skip features by retrieving patch-level prototypes from a shared memory and adaptively integrating them into the current representation, which helps maintain stable local contrast cues under diverse scene backgrounds. Experiments on three public benchmarks show that RDANet achieves the best performance on most evaluation metrics, while scale- and background-stratified evaluations indicate more stable behavior across target sizes and scene complexity. The code is available at https://github.com/BIT-RuiLiu/RDANet.
CommentsAccept by TGRS 2026
