  Single-frame InfraRed Small Target (SIRST) detection has been a challenging
task due to a lack of inherent characteristics, imprecise bounding box
regression, a scarcity of real-world datasets, and sensitive localization
evaluation. In this paper, we propose a comprehensive solution to these
challenges. First, we find that the existing anchor-free label assignment
method is prone to mislabeling small targets as background, leading to their
omission by detectors. To overcome this issue, we propose an all-scale
pseudo-box-based label assignment scheme that relaxes the constraints on scale
and decouples the spatial assignment from the size of the ground-truth target.
Second, motivated by the structured prior of feature pyramids, we introduce the
one-stage cascade refinement network (OSCAR), which uses the high-level head as
soft proposals for the low-level refinement head. This allows OSCAR to process
the same target in a cascade coarse-to-fine manner. Finally, we present a new
research benchmark for infrared small target detection, consisting of the
SIRST-V2 dataset of real-world, high-resolution single-frame targets, the
normalized contrast evaluation metric, and the DeepInfrared toolkit for
detection. We conduct extensive ablation studies to evaluate the components of
OSCAR and compare its performance to state-of-the-art model-driven and
data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a
top-down cascade refinement framework can improve the accuracy of infrared
small target detection without sacrificing efficiency. The DeepInfrared
toolkit, dataset, and trained models are available at
https://github.com/YimianDai/open-deepinfrared to advance further research in
this field.
