Abstract
Infrared small target detection (ISTD) plays a critical role in a wide range of civilian and military applications. Existing methods suffer from deficiencies in the localization of dim targets and the perception of contour information under dense clutter environments, severely limiting their detection performance. To tackle these issues, we propose a contour-aware and saliency priors embedding network (CSPENet) for ISTD. We first design a surround-convergent prior extraction module (SCPEM) that effectively captures the intrinsic characteristic of target contour pixel gradients converging toward their center. This module concurrently extracts two collaborative priors: a boosted saliency prior for accurate target localization and multi-scale structural priors for comprehensively enriching contour detail representation. Building upon this, we propose a dual-branch priors embedding architecture (DBPEA) that establishes differentiated feature fusion pathways, embedding these two priors at optimal network positions to achieve performance enhancement. Finally, we develop an attention-guided feature enhancement module (AGFEM) to refine feature representations and improve saliency estimation accuracy. Experimental results on public datasets NUDT-SIRST, IRSTD-1k, and NUAA-SIRST demonstrate that our CSPENet outperforms other state-of-the-art methods in detection performance. The code is available at this https URL.
Abstract (translated)
红外小目标检测（ISTD）在众多民用和军事应用中扮演着关键角色。现有的方法在定位暗淡目标以及在密集杂波环境中感知轮廓信息方面存在缺陷，这严重限制了它们的检测性能。为了解决这些问题，我们提出了一种基于轮廓感知和显著性先验嵌入的网络（CSPENet）用于ISTD。首先，我们设计了一个环绕汇聚先验提取模块（SCPEM），该模块能够有效捕捉目标轮廓像素梯度向中心汇聚的内在特性。此模块同时提取两种协同工作的先验：一种是增强型显著性先验，有助于精确的目标定位；另一种是多尺度结构化先验，用于全面丰富轮廓细节表示。 在此基础上，我们提出了一种双支路先验嵌入架构（DBPEA），建立了差异化特征融合路径，在最佳网络位置嵌入这两种先验以实现性能提升。最后，我们开发了一个基于注意力引导的特征增强模块（AGFEM）来优化特征表示并提高显著性估计的准确性。 在公开数据集NUDT-SIRST、IRSTD-1k和NUAA-SIRST上的实验结果表明，我们的CSPENet在检测性能上优于其他最先进的方法。代码可在[此处](https://example.com)获取（请将URL更改为实际链接）。
URL
https://arxiv.org/abs/2505.09943
