  Limited by equipment limitations and the lack of target intrinsic features,
existing infrared small target detection methods have difficulty meeting actual
comprehensive performance requirements. Therefore, we propose an innovative
lightweight and robust network (LR-Net), which abandons the complex structure
and achieves an effective balance between detection accuracy and resource
consumption. Specifically, to ensure the lightweight and robustness, on the one
hand, we construct a lightweight feature extraction attention (LFEA) module,
which can fully extract target features and strengthen information interaction
across channels. On the other hand, we construct a simple refined feature
transfer (RFT) module. Compared with direct cross-layer connections, the RFT
module can improve the network's feature refinement extraction capability with
little resource consumption. Meanwhile, to solve the problem of small target
loss in high-level feature maps, on the one hand, we propose a low-level
feature distribution (LFD) strategy to use low-level features to supplement the
information of high-level features. On the other hand, we introduce an
efficient simplified bilinear interpolation attention module (SBAM) to promote
the guidance constraints of low-level features on high-level features and the
fusion of the two. In addition, We abandon the traditional resizing method and
adopt a new training and inference cropping strategy, which is more robust to
datasets with multi-scale samples. Extensive experimental results show that our
LR-Net achieves state-of-the-art (SOTA) performance. Notably, on the basis of
the proposed LR-Net, we achieve 3rd place in the "ICPR 2024 Resource-Limited
Infrared Small Target Detection Challenge Track 2: Lightweight Infrared Small
Target Detection".
