Small targets are often submerged in cluttered backgrounds of infrared images. Conventional detectors tend to generate false alarms, while CNN-based detectors lose small targets in deep layers. To this end, we propose iSmallNet, a multi-stream densely nested network with label decoupling for infrared small object detection. On the one hand, to fully exploit the shape information of small targets, we decouple the original labeled ground-truth (GT) map into an interior map and a boundary one. The GT map, in collaboration with the two additional maps, tackles the unbalanced distribution of small object boundaries. On the other hand, two key modules are delicately designed and incorporated into the proposed network to boost the overall performance. First, to maintain small targets in deep layers, we develop a multi-scale nested interaction module to explore a wide range of context information. Second, we develop an interior-boundary fusion module to integrate multi-granularity information. Experiments on NUAA-SIRST and NUDT-SIRST clearly show the superiority of iSmallNet over 11 state-of-the-art detectors.

翻译：小目标常淹没于红外图像的杂乱背景中。传统检测器易产生虚警，而基于CNN的检测器在深层网络中会丢失小目标。为此，我们提出iSmallNet——一种面向红外小目标检测的多流密集嵌套网络与标签解耦方法。一方面，为充分挖掘小目标的形状信息，我们将原始标注的真实值（GT）图解耦为内部图和边界图。GT图与这两个附加图协同作用，解决小目标边界分布不均衡问题。另一方面，我们精心设计并集成两个关键模块以提升网络整体性能：首先，为在深层网络中保留小目标，我们开发了多尺度嵌套交互模块以探索广泛的上下文信息；其次，提出内部-边界融合模块以整合多粒度信息。在NUAA-SIRST与NUDT-SIRST数据集上的实验表明，iSmallNet显著优于11种最先进的检测器。