Infrared small object detection (ISOS) aims to segment small objects only covered with several pixels from clutter background in infrared images. It's of great challenge due to: 1) small objects lack of sufficient intensity, shape and texture information; 2) small objects are easily lost in the process where detection models, say deep neural networks, obtain high-level semantic features and image-level receptive fields through successive downsampling. This paper proposes a reliable detection model for ISOS, dubbed UCFNet, which can handle well the two issues. It builds upon central difference convolution (CDC) and fast Fourier convolution (FFC). On one hand, CDC can effectively guide the network to learn the contrast information between small objects and the background, as the contrast information is very essential in human visual system dealing with the ISOS task. On the other hand, FFC can gain image-level receptive fields and extract global information while preventing small objects from being overwhelmed.Experiments on several public datasets demonstrate that our method significantly outperforms the state-of-the-art ISOS models, and can provide useful guidelines for designing better ISOS deep models. Codes will be available soon.

翻译：红外小目标检测旨在从红外图像的杂波背景中分割仅覆盖几个像素的小目标。该任务极具挑战性，原因在于：1）小目标缺乏足够的强度、形状和纹理信息；2）在检测模型（如深度神经网络）通过连续下采样获取高层语义特征和图像级感受野的过程中，小目标容易丢失。本文提出一种可靠的ISOS检测模型，命名为UCFNet，该模型能够很好地处理上述两个问题。模型基于中心差分卷积（CDC）和快速傅里叶卷积（FFC）构建。一方面，CDC能够有效引导网络学习小目标与背景之间的对比信息，因为对比信息在人类视觉系统处理ISOS任务时至关重要。另一方面，FFC能够在防止小目标被淹没的同时，获取图像级感受野并提取全局信息。在多个公开数据集上的实验表明，我们的方法显著优于当前最先进的ISOS模型，并为设计更优的ISOS深度模型提供了有益指导。代码即将公开。