Infrared dim small target (IDST) detection holds significant importance in early target warning and ground monitoring. However, IDST detection remains a long-standing challenge due to the low signal-to-noise ratio and low contrast. Feature fusion is an effective approach for feature enrichment and improving poor performance in IDST detection. Existing feature fusion methods tend to overlook the importance of focusing on both multilayer and single-layer features, which prevents target features from being fully exploited, resulting in suboptimal outcomes for IDST detection. In this article, we present a multiangle pyramid feature fusion (MAPFF) network, which selects fusion objects from multiple perspectives and then fuses them. Namely, multilayer features and single-layer features—two perspectives of the fusion object—are selected and fused separately. The MAPFF network consists of two primary modules: a cross-layer complementary feature (CLCF) module and an atrous spatial pyramid pooling with attention (AttnASPP) module. To effectively fuse semantic and geometric detail information, the CLCF module adaptively combines different layer features as complementary features, while the original layer features serve as the main features. Concurrently, through channel shuffle, the complementary and main features achieve substantial information exchange. The AttnASPP module employs parallel atrous convolutions with multiple dilation rates to obtain multiscale information and incorporates an attention mechanism to emphasize effective features. Experimental results on the SIATD, SIRST, and IRSTD_1k datasets demonstrate that our method can precisely identify IDSTs, significantly reduce the false alarm rate, and outperform other methods.

中文翻译：

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MAPFF：用于红外弱小目标检测的多角度金字塔特征融合网络

红外微弱小目标（IDST）检测在早期目标预警和地面监测中具有重要意义。然而，由于信噪比低和对比度低，IDST 检测仍然是一个长期存在的挑战。特征融合是特征丰富和改善 IDST 检测不良性能的有效方法。现有的特征融合方法往往忽视了同时关注多层和单层特征的重要性，这阻碍了目标特征被充分利用，导致 IDST 检测结果不理想。在本文中，我们提出了一种多角度金字塔特征融合（MAPFF）网络，该网络从多个角度选择融合对象，然后将它们融合。即，分别选择多层特征和单层特征（融合对象的两个视角）并进行融合。 MAPFF 网络由两个主要模块组成：跨层互补特征（CLCF）模块和带注意力的多孔空间金字塔池（AttnASPP）模块。为了有效融合语义和几何细节信息，CLCF模块自适应地将不同层特征组合为互补特征，而原始层特征作为主要特征。同时，通过渠道洗牌，互补特征和主特征实现实质性的信息交换。 AttnASPP 模块采用具有多个扩张率的并行空洞卷积来获取多尺度信息，并结合注意力机制来强调有效特征。在SIATD、SIRST和IRSTD_1k数据集上的实验结果表明，我们的方法可以精确识别IDST，显着降低误报率，并且优于其他方法。