  Infrared small-target detection (ISTD) is an important computer vision task.
ISTD aims at separating small targets from complex background clutter. The
infrared radiation decays over distances, making the targets highly dim and
prone to confusion with the background clutter, which makes the detector
challenging to balance the precision and recall rate. To deal with this
difficulty, this paper proposes a neural-network-based ISTD method called
CourtNet, which has three sub-networks: the prosecution network is designed for
improving the recall rate; the defendant network is devoted to increasing the
precision rate; the jury network weights their results to adaptively balance
the precision and recall rate. Furthermore, the prosecution network utilizes a
densely connected transformer structure, which can prevent small targets from
disappearing in the network forward propagation. In addition, a fine-grained
attention module is adopted to accurately locate the small targets.
Experimental results show that CourtNet achieves the best F1-score on the two
ISTD datasets, MFIRST (0.62) and SIRST (0.73).
