  Infrared small target detection (IRSTD) tasks are extremely challenging for
two main reasons: 1) it is difficult to obtain accurate labelling information
that is critical to existing methods, and 2) infrared (IR) small target
information is easily lost in deep networks. To address these issues, we
propose a single-point supervised high-resolution dynamic network (SSHD-Net).
In contrast to existing methods, we achieve state-of-the-art (SOTA) detection
performance using only single-point supervision. Specifically, we first design
a high-resolution cross-feature extraction module (HCEM), that achieves
bi-directional feature interaction through stepped feature cascade channels
(SFCC). It balances network depth and feature resolution to maintain deep IR
small-target information. Secondly, the effective integration of global and
local features is achieved through the dynamic coordinate fusion module (DCFM),
which enhances the anti-interference ability in complex backgrounds. In
addition, we introduce the high-resolution multilevel residual module (HMRM) to
enhance the semantic information extraction capability. Finally, we design the
adaptive target localization detection head (ATLDH) to improve detection
accuracy. Experiments on the publicly available datasets NUDT-SIRST and
IRSTD-1k demonstrate the effectiveness of our method. Compared to other SOTA
methods, our method can achieve better detection performance with only a single
point of supervision.
