  Learning-based infrared small object detection methods currently rely heavily
on the classification backbone network. This tends to result in tiny object
loss and feature distinguishability limitations as the network depth increases.
Furthermore, small objects in infrared images are frequently emerged bright and
dark, posing severe demands for obtaining precise object contrast information.
For this reason, we in this paper propose a simple and effective ``U-Net in
U-Net'' framework, UIU-Net for short, and detect small objects in infrared
images. As the name suggests, UIU-Net embeds a tiny U-Net into a larger U-Net
backbone, enabling the multi-level and multi-scale representation learning of
objects. Moreover, UIU-Net can be trained from scratch, and the learned
features can enhance global and local contrast information effectively. More
specifically, the UIU-Net model is divided into two modules: the
resolution-maintenance deep supervision (RM-DS) module and the
interactive-cross attention (IC-A) module. RM-DS integrates Residual U-blocks
into a deep supervision network to generate deep multi-scale
resolution-maintenance features while learning global context information.
Further, IC-A encodes the local context information between the low-level
details and high-level semantic features. Extensive experiments conducted on
two infrared single-frame image datasets, i.e., SIRST and Synthetic datasets,
show the effectiveness and superiority of the proposed UIU-Net in comparison
with several state-of-the-art infrared small object detection methods. The
proposed UIU-Net also produces powerful generalization performance for video
sequence infrared small object datasets, e.g., ATR ground/air video sequence
dataset. The codes of this work are available openly at
\url{https://github.com/danfenghong/IEEE_TIP_UIU-Net}.
