Abstract
Infrared small target detection (ISTD) is vital for long-range surveillance in military, maritime, and early warning applications. ISTD is challenged by targets occupying less than 0.15% of the image and low distinguishability from complex backgrounds. Existing deep learning methods often suffer from information loss during downsampling and inefficient global context modeling. This paper presents SAMamba, a novel framework integrating SAM2's hierarchical feature learning with Mamba's selective sequence modeling. Key innovations include: (1) A Feature Selection Adapter (FS-Adapter) for efficient natural-to-infrared domain adaptation via dual-stage selection (token-level with a learnable task embedding and channel-wise adaptive transformations); (2) A Cross-Channel State-Space Interaction (CSI) module for efficient global context modeling with linear complexity using selective state space modeling; and (3) A Detail-Preserving Contextual Fusion (DPCF) module that adaptively combines multi-scale features with a gating mechanism to balance high-resolution and low-resolution feature contributions. SAMamba addresses core ISTD challenges by bridging the domain gap, maintaining fine-grained details, and efficiently modeling long-range dependencies. Experiments on NUAA-SIRST, IRSTD-1k, and NUDT-SIRST datasets show SAMamba significantly outperforms state-of-the-art methods, especially in challenging scenarios with heterogeneous backgrounds and varying target scales. Code: this https URL.
Abstract (translated)
红外小目标检测（ISTD）在军事、海事和早期预警应用中的远程监控中至关重要。ISTD面临的挑战是，目标通常只占据图像的0.15%以下，并且难以从复杂的背景中区分出来。现有的深度学习方法往往因为在下采样过程中信息丢失以及全局上下文建模效率低下而受到影响。本文介绍了SAMamba这一新颖框架，它将SAM2的分层特征学习与Mamba的选择性序列建模相结合。 该框架的关键创新包括： 1. 一种通过双阶段选择（标记级具有可学习的任务嵌入和通道适应性转换）实现高效自然到红外领域适应性的特征选择适配器（FS-Adapter）。 2. 一个利用选择状态空间建模以线性复杂度进行有效全局上下文建模的跨通道状态空间交互模块(CSI)。 3. 一种自适应组合多尺度特征并采用门控机制平衡高分辨率与低分辨率特征贡献的细节保留上下文融合（DPCF）模块。 SAMamba通过弥合领域差距、保持细粒度详情以及有效地对远程依赖性建模，解决了ISTD的核心挑战。在NUAA-SIRST、IRSTD-1k和NUDT-SIRST数据集上的实验表明，SAMamba显著优于现有最先进的方法，尤其是在背景复杂且目标尺度多变的具有挑战性的场景中表现尤为突出。 代码链接：[请在此处插入实际链接]
URL
https://arxiv.org/abs/2505.23214
