Abstract
Infrared small target detection is currently a hot and challenging task in computer vision. Existing methods usually focus on mining visual features of targets, which struggles to cope with complex and diverse detection scenarios. The main reason is that infrared small targets have limited image information on their own, thus relying only on visual features fails to discriminate targets and interferences, leading to lower detection performance. To address this issue, we introduce a novel approach leveraging semantic text to guide infrared small target detection, called Text-IRSTD. It innovatively expands classical IRSTD to text-guided IRSTD, providing a new research idea. On the one hand, we devise a novel fuzzy semantic text prompt to accommodate ambiguous target categories. On the other hand, we propose a progressive cross-modal semantic interaction decoder (PCSID) to facilitate information fusion between texts and images. In addition, we construct a new benchmark consisting of 2,755 infrared images of different scenarios with fuzzy semantic textual annotations, called FZDT. Extensive experimental results demonstrate that our method achieves better detection performance and target contour recovery than the state-of-the-art methods. Moreover, proposed Text-IRSTD shows strong generalization and wide application prospects in unseen detection scenarios. The dataset and code will be publicly released after acceptance of this paper.
Abstract (translated)
红外小目标检测目前是计算机视觉领域的一个热点和具有挑战性的任务。现有方法通常侧重于挖掘目标的视觉特征，这在面对复杂多样的检测场景时显得力不从心。主要原因在于红外小目标自身图像信息有限，仅仅依赖视觉特征难以区分目标与干扰物，导致检测性能较低。为解决这一问题，我们提出了一种利用语义文本引导红外小目标检测的新方法，称为Text-IRSTD（文本辅助的红外小目标检测）。该方法创新性地将传统的IRSTD扩展到文本指导下的IRSTD，提供了一种新的研究思路。 一方面，我们设计了一种新颖的模糊语义文本提示来适应模棱两可的目标类别。另一方面，我们提出了一个逐步跨模态语义交互解码器（PCSID），以促进文本和图像之间的信息融合。此外，我们构建了一个包含2,755张不同场景下红外图像的新基准数据集，并使用模糊语义文本注释标记为FZDT。 广泛的实验结果表明，我们的方法在检测性能和目标轮廓恢复方面优于现有最先进的方法。另外，所提出的Text-IRSTD表现出较强的一般化能力和广泛的应用前景，在未见过的检测场景中亦有卓越表现。该数据集和代码将在论文接受后公开发布。
URL
https://arxiv.org/abs/2503.07249
