The identification of tiny objects via single-frame infrared is a significant challenge in computer vision, primarily due to large variances in target dimensions, overcrowded backgrounds, suboptimal signal-to-noise ratios, the propensity of bounding box regression to vary with target size, and potential partial occlusion scenarios. Addressing these challenges, we propose a sturdy network for enhancing features in the infrared detection of small targets utilizing multi-scale attention. In particular, the introduction of the Iterative Attentional Feature Fusion (iAFF) module at the detection network’s neck aims to tackle the issue of minor target features being overshadowed in the process of cross-scale feature fusion. Additionally, we present the Occlusion-Aware Attention Module (OAAM), which demonstrates greater tolerance for target localization errors in regions where local features are missing due to partial occlusion. By combining the scale and spatial attention mechanisms of the Dynamic Head, our approach adaptively learns the relative importance of different semantic layers. Furthermore, the integration of Normalized Wasserstein–Gaussian Distance (NWD) aims to tackle the convergence issues associated with the increased sensitivity of bounding box regression in identifying minor infrared targets. For assessing our technique’s efficiency, we present a novel benchmark dataset, IRMT-UAV, noted for its considerable discrepancies in target size, intricate backdrops, and substantial variations in the signal-to-noise ratio. The outcomes of our experiments using the public IRSTD-1k dataset and the internally developed IRMT-UAV dataset reveal that our technique surpasses cutting-edge (SOTA) methods, with mAP50 enhancements of 1.4% and 4.9%, respectively, thus proving our method’s efficiency and sturdiness.

Detecting small targets in infrared imagery remains highly challenging due to sub-pixel target sizes, low signal-to-noise ratios, and complex background clutter. This paper proposes PSHNet, a hybrid deep-learning framework that combines dense spatial heatmap supervision with geometry-aware regression for accurate infrared small-target detection. The network generates position–scale heatmaps to guide coarse localization, which are further refined through sub-pixel offset and size regression. A Complete IoU (CIoU) loss is introduced as a geometric regularization term to improve alignment between predicted and ground-truth bounding boxes. To better preserve fine spatial details essential for identifying small thermal signatures, an Enhanced Low-level Feature Module (ELFM) is incorporated using multi-scale dilated convolutions and channel attention. Experiments on the NUDT-SIRST and IRSTD-1k datasets demonstrate that PSHNet outperforms existing methods in IoU, detection probability, and false alarm rate, achieving IoU improvement and robust performance under low-SNR conditions.