Abstract
Infrared small target detection (ISTD) is highly sensitive to sensor type, observation conditions, and the intrinsic properties of the target. These factors can introduce substantial variations in the distribution of acquired infrared image data, a phenomenon known as domain shift. Such distribution discrepancies significantly hinder the generalization capability of ISTD models across diverse scenarios. To tackle this challenge, this paper introduces an ISTD framework enhanced by domain adaptation. To alleviate distribution shift between datasets and achieve cross-sample alignment, we introduce Cross-view Channel Alignment (CCA). Additionally, we propose the Cross-view Top-K Fusion strategy, which integrates target information with diverse background features, enhancing the model' s ability to extract critical data characteristics. To further mitigate the impact of noise on ISTD, we develop a Noise-guided Representation learning strategy. This approach enables the model to learn more noise-resistant feature representations, to improve its generalization capability across diverse noisy domains. Finally, we develop a dedicated infrared small target dataset, RealScene-ISTD. Compared to state-of-the-art methods, our approach demonstrates superior performance in terms of detection probability (Pd), false alarm rate (Fa), and intersection over union (IoU). The code is available at: this https URL.
Abstract (translated)
红外小目标检测（ISTD）对传感器类型、观测条件以及目标本身的固有属性非常敏感。这些因素会导致获取的红外图像数据分布出现显著变化，这一现象被称为领域偏移（domain shift）。这种分布差异极大地阻碍了ISTD模型在不同场景下的泛化能力。 为了解决这一挑战，本文提出了一种通过领域适应增强的ISTD框架。为了减轻不同数据集之间的分布偏差并实现跨样本对齐，我们引入了跨视图通道对齐（CCA）技术。此外，我们还提出了跨视图Top-K融合策略，该策略将目标信息与多样化的背景特征相结合，以提升模型提取关键数据特性的能力。为进一步降低噪声对ISTD的影响，我们开发了一种基于噪声引导的表示学习策略。这一方法使模型能够学习更具抗噪性的特征表示，从而提高其在各种嘈杂环境下的泛化性能。 最后，我们创建了一个专门用于红外小目标检测的数据集——RealScene-ISTD。与现有最佳方法相比，我们的方法在检测概率（Pd）、虚警率（Fa）和交并比（IoU）等方面均表现出色。代码可在以下链接获取：[this https URL]。
URL
https://arxiv.org/abs/2504.16487
