论文标题
ISMALLNET：密集的嵌套网络，带有标签的脱钩，用于红外小目标检测
iSmallNet: Densely Nested Network with Label Decoupling for Infrared Small Target Detection
论文作者
论文摘要
小目标通常被浸入红外图像的混乱背景中。常规检测器倾向于产生错误的警报，而基于CNN的探测器在深层中失去了小目标。为此，我们提出了ISMALLNET，这是一种多通嵌套的网络，具有标记为红外小对象检测的标签。一方面，为了充分利用小目标的形状信息，我们将原始标记的地面真相（GT）映射切成内部图和边界图。 GT地图与另外两个地图合作解决了小物体边界的不平衡分布。另一方面，两个关键的模块经过精心设计和整合到拟议的网络中，以提高整体性能。首先，为了在深层中保持小目标，我们开发了一个多尺度的嵌套交互模块，以探索广泛的上下文信息。其次，我们开发了一个内部边界融合模块，以整合多晶型信息。在NUAA-SIRST和NUDT-SIRST上进行的实验清楚地表明了Ismallnet的优越性，而不是11个最先进的探测器。
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.
