Infrared small target detection (IRSTD) is a task in target detection and computer vision that remains challenging but also critical. The cause of its complexity and difficulty lies in the inherent features of this class of targets, as most of the dataset has extreme class imbalance, weak classification contrast, and complex noise clutter in the background. Focusing on these existing issues, this work proposes CG-IRNet, a structure-aware detection framework that integrates multi-scale feature aggregation with Structure–Confidence Hybrid (SCH) loss, which integrates an augmented variant of confidence-aware Scale–Location Sensitive (SLS) loss with instance-wise structural supervision and a confidence-guided background suppression mechanism, which are all targeted towards enhancing localization consistency while largely reducing false alarms. In addition to these, a frequency-aware feature refinement module is incorporated to strengthen small target saliency under highly cluttered scenes. This work included a series of extensive experiments across three benchmark datasets included in SIRST, namely IRSTD-1K, NUAA-SIRST, and NUDT-SIRST. These experiments demonstrate a superior trade-off between detection probability (Pd) and false alarm rate. On IRSTD-1K, CG-IRNet achieves 65.09 mIoU and reduces the false alarm rate to 30.992 × 10−6, which is significantly lower than SCTransNet (55.74 × 10−6) at the same detection probability (93.27%). On NUAA-SIRST and NUDT-SIRST, the proposed method achieves 96.95% and 98.62% detection probability, respectively, while maintaining competitive or lower false alarm rates under challenging background conditions. These outcomes effectively demonstrate the improvements achieved in this work and the effectiveness of the proposed confidence-guided suppression and structure-aware optimization. Also included in the group of experiments performed in this work is the ablation study on model hyperparameters and qualitative analyses, which further confirm the joint improvements contributed by the proposed structural supervision and confidence-aware design, particularly in regimes where a low false alarm rate is the goal of optimization.
Zhu et al. (Mon,) studied this question.