  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. Code are
available at https://github.com/wcyjerry/BasicISOS.
