发表机构
Jiangxi University of Finance and Economics; Tiangong University（江西财经大学; 天工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对红外小目标检测中精度与虚警难以平衡的问题，提出SANet，通过双路径语义模块和选择性注意力融合增强特征判别，在三个基准上IoU显著领先。
AI 中文摘要
红外小目标检测旨在复杂背景下准确识别和定位暗淡目标，并支持海上监视和军事搜救等应用。然而，红外目标的小尺寸和弱对比度使得在检测精度和虚警之间取得平衡变得困难。本文提出了一种用于红外小目标检测的选择性注意力网络（SANet）。一个双路径语义感知模块结合了标准卷积和针状卷积，以保持局部空间一致性并捕获更广泛的上下文信息。空间和通道注意力进一步细化特征并提高目标与背景的判别能力。为了解决U-Net中静态跳跃连接的限制，一个选择性注意力融合模块使用空间变化权重自适应地跨尺度整合特征。它选择性地增强显著区域，并提高真实目标与虚警之间的判别能力。在三个公开基准数据集NUAA-SIRST、IRSTD-1K和NUDT-SIRST上的实验表明，SANet在交并比（IoU）、归一化IoU、检测概率和虚警率方面取得了有竞争力的性能。其IoU分别比第二好的方法高出1.93、4.32和2.21个百分点。这些结果支持了SANet在暗淡目标感知、判别性特征表示和背景抑制方面的有效性。
英文摘要
Infrared small target detection aims to accurately identify and locate dim targets in complex backgrounds and supports applications such as maritime surveillance and military search and rescue. However, the small size and weak contrast of infrared targets make it difficult to balance detection accuracy and false alarms. This paper proposes a selective attention network (SANet) for infrared small target detection. A dual-path semantic-aware module combines standard and pinwheel-shaped convolutions to preserve local spatial consistency and capture broader contextual information. Spatial and channel attention further refine the features and improve target-background discrimination. To address the limitations of static skip connections in U-Net, a selective attention fusion module adaptively integrates features across scales using spatially varying weights. It selectively enhances salient regions and improves discrimination between true targets and false alarms. Experiments on three public benchmarks, NUAA-SIRST, IRSTD-1K, and NUDT-SIRST, show that SANet achieves competitive performance in intersection over union (IoU), normalized IoU, detection probability, and false alarm rate. Its IoU exceeds that of the second-best method by 1.93, 4.32, and 2.21 percentage points, respectively. These results support the effectiveness of SANet in dim-target perception, discriminative feature representation, and background suppression.
