Infrared small target detection in dynamic scenes remains challenging due to the highly coupled motions among targets, imaging platforms, and dynamic backgrounds. Existing multi-frame methods usually perform implicit temporal modeling, where coherent background dynamics dominate motion correspondence learning, leading to an inherent trade-off between detection and false alarms. In this work, we observe that background motions exhibit strong global coherence, whereas small targets mainly correspond to sparse local motion anomalies. Moreover, many false-alarm responses maintain high consistency with globally coherent motion patterns, indicating that they mainly originate from coherent background dynamics rather than genuine target motions. Based on these observations, we propose a decoupled motion representation learning framework for moving infrared small target detection. Specifically, an explicit motion branch is introduced to model globally coherent motion dynamics using pretrained optical flow priors, together with a structure-preserving self-supervised adaptation strategy for infrared motion correspondence learning. Meanwhile, an implicit motion branch based on deformable feature alignment is designed to capture target-sensitive local motion anomalies under coherent motion guidance. Furthermore, a coherent-motion-guided local anomaly reasoning module is proposed to identify and suppress coherent-motion-induced false responses during localized motion modeling. Extensive experiments on two challenging infrared small target detection benchmarks demonstrate that the proposed method consistently outperforms existing state-of-the-art approaches, particularly in dynamic scenes with complex motions, while maintaining favorable inference efficiency.

翻译：动态场景中的红外小目标检测因目标、成像平台和动态背景之间的高度耦合运动而仍然具有挑战性。现有基于多帧的方法通常进行隐式时间建模，其中连贯的背景动态主导着运动对应学习，导致检测与虚警之间存在固有的权衡。在本工作中，我们观察到背景运动表现出强烈的全局连贯性，而小目标主要对应于稀疏的局部运动异常。此外，许多虚警响应与全局连贯运动模式保持高度一致性，表明它们主要源于连贯的背景动态，而非真实目标运动。基于这些观察，我们提出了一种用于移动红外小目标检测的解耦运动表示学习框架。具体而言，引入了一个显式运动分支，利用预训练的光流先验建模全局连贯的运动动态，并结合一种保持结构的自监督适应策略进行红外运动对应学习。同时，设计了一个基于可变形特征对齐的隐式运动分支，在连贯运动引导下捕捉目标敏感的局部运动异常。此外，提出了一个连贯运动引导的局部异常推理模块，用于在局部运动建模中识别并抑制由连贯运动引起的虚假响应。在两个具有挑战性的红外小目标检测基准上的大量实验表明，所提方法在具有复杂运动的动态场景中始终优于现有最先进方法，同时保持了良好的推理效率。