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.

本文提出了一种新型可靠的ISOS检测模型UCFNet，该模型可以有效地解决IR图像中小物体缺乏足够的强度、形状和质地信息以及在深度神经网络模型中获取高级语义特征和图像级感受野的过程中可能丢失的问题。同时，这种模型借助于中心差分卷积和快速傅里叶卷积，在不被小目标挤压的情况下提取全局信息。我们在几个公共数据集上进行了实验，并证明了我们的方法明显优于现有的 ISOS 模型。

局部对比和全局语境信息使红外小物体再次显著