  Single-frame infrared small target detection is considered to be a
challenging task, due to the extreme imbalance between target and background,
bounding box regression is extremely sensitive to infrared small target, and
target information is easy to lose in the high-level semantic layer. In this
article, we propose an enhancing feature learning network (EFLNet) to address
these problems. First, we notice that there is an extremely imbalance between
the target and the background in the infrared image, which makes the model pay
more attention to the background features rather than target features. To
address this problem, we propose a new adaptive threshold focal loss (ATFL)
function that decouples the target and the background, and utilizes the
adaptive mechanism to adjust the loss weight to force the model to allocate
more attention to target features. Second, we introduce the normalized Gaussian
Wasserstein distance (NWD) to alleviate the difficulty of convergence caused by
the extreme sensitivity of the bounding box regression to infrared small
target. Finally, we incorporate a dynamic head mechanism into the network to
enable adaptive learning of the relative importance of each semantic layer.
Experimental results demonstrate our method can achieve better performance in
the detection performance of infrared small target compared to the
state-of-the-art (SOTA) deep-learning-based methods. The source codes and
bounding box annotated datasets are available at
https://github.com/YangBo0411/infrared-small-target.
