  Infrared small target detection is a technique for finding small targets from
infrared clutter background. Due to the dearth of high-level semantic
information, small infrared target features are weakened in the deep layers of
the CNN, which underachieves the CNN's representation ability. To address the
above problem, in this paper, we propose an infrared low-level network (ILNet)
that considers infrared small targets as salient areas with little semantic
information. Unlike other SOTA methods, ILNet pays greater attention to
low-level information instead of treating them equally. A new lightweight
feature fusion module, named Interactive Polarized Orthogonal Fusion module
(IPOF), is proposed, which integrates more important low-level features from
the shallow layers into the deep layers. A Dynamic One-Dimensional Aggregation
layers (DODA) are inserted into the IPOF, to dynamically adjust the aggregation
of low dimensional information according to the number of input channels. In
addition, the idea of ensemble learning is used to design a Representative
Block (RB) to dynamically allocate weights for shallow and deep layers.
Experimental results on the challenging NUAA-SIRST (78.22% nIoU and 1.33e-6 Fa)
and IRSTD-1K (68.91% nIoU and 3.23e-6 Fa) dataset demonstrate that the proposed
ILNet can get better performances than other SOTA methods. Moreover, ILNet can
obtain a greater improvement with the increasement of data volume. Training
code are available at https://github.com/Li-Haoqing/ILNet.
