  Infrared small object detection is an important computer vision task
involving the recognition and localization of tiny objects in infrared images,
which usually contain only a few pixels. However, it encounters difficulties
due to the diminutive size of the objects and the generally complex backgrounds
in infrared images. In this paper, we propose a deep learning method, HCF-Net,
that significantly improves infrared small object detection performance through
multiple practical modules. Specifically, it includes the parallelized
patch-aware attention (PPA) module, dimension-aware selective integration
(DASI) module, and multi-dilated channel refiner (MDCR) module. The PPA module
uses a multi-branch feature extraction strategy to capture feature information
at different scales and levels. The DASI module enables adaptive channel
selection and fusion. The MDCR module captures spatial features of different
receptive field ranges through multiple depth-separable convolutional layers.
Extensive experimental results on the SIRST infrared single-frame image dataset
show that the proposed HCF-Net performs well, surpassing other traditional and
deep learning models. Code is available at
https://github.com/zhengshuchen/HCFNet.
