  Infrared small target detection faces the problem that it is difficult to
effectively separate the background and the target. Existing deep
learning-based methods focus on appearance features and ignore high-frequency
directional features. Therefore, we propose a multi-scale direction-aware
network (MSDA-Net), which is the first attempt to integrate the high-frequency
directional features of infrared small targets as domain prior knowledge into
neural networks. Specifically, an innovative multi-directional feature
awareness (MDFA) module is constructed, which fully utilizes the prior
knowledge of targets and emphasizes the focus on high-frequency directional
features. On this basis, combined with the multi-scale local relation learning
(MLRL) module, a multi-scale direction-aware (MSDA) module is further
constructed. The MSDA module promotes the full extraction of local relations at
different scales and the full perception of key features in different
directions. Meanwhile, a high-frequency direction injection (HFDI) module
without training parameters is constructed to inject the high-frequency
directional information of the original image into the network. This helps
guide the network to pay attention to detailed information such as target edges
and shapes. In addition, we propose a feature aggregation (FA) structure that
aggregates multi-level features to solve the problem of small targets
disappearing in deep feature maps. Furthermore, a lightweight feature alignment
fusion (FAF) module is constructed, which can effectively alleviate the pixel
offset existing in multi-level feature map fusion. Extensive experimental
results show that our MSDA-Net achieves state-of-the-art (SOTA) results on the
public NUDT-SIRST, SIRST and IRSTD-1k datasets.
