Infrared small-target detection is commonly implemented as binary pixel-level segmentation and localization but remains challenging because targets occupy very few pixels and are embedded in complex backgrounds. This paper develops the Semantic-Guided Enhanced UIU-Net (SGUIU-Net) as a task-specific extension of U-Net in U-Net (UIU-Net) by combining Dilated Residual U-blocks (RSUs), the Convolutional Block Attention Module (CBAM), Focal–Dice deep supervision, and Semantic-Guided Cross-Attention Fusion (SGCA-Fusion). Experiments are conducted on the 427-image SIRST benchmark and the auxiliary IRSTD-1K and SIRST-V2 datasets. On SIRST, the full model obtains an IoU of 0.7995 ± 0.0011 and an nIoU of 0.7572 ± 0.0012 over three independent runs, improving the matched UIU-Net baseline means by 0.0180 and 0.0057, respectively. The model also improves IoU/nIoU from 0.6379/0.6057 to 0.7125/0.6787 on IRSTD-1K and from 0.7215/0.6832 to 0.7364/0.6937 on SIRST-V2. These gains are accompanied by an increase from 50.54 M to 106.31 M parameters and a decrease from 50.05 to 32.53 FPS at 320 × 320 resolution.

UAV radio-frequency (RF) signal identification is an important technique for low-altitude security monitoring. However, background noise interference and distortions in amplitude and phase features can reduce the robustness of methods based on a single frequency-domain representation. A one-dimensional power spectrum alone provides limited capability for characterizing pairwise relationships among frequency components, whereas two-dimensional feature representations may weaken spectral-line continuity. To address these limitations, this paper proposes a Power Spectral Density-Frequency-domain Gramian Angular Field Dual-stream Fusion Network (PFG-DSFNet). First, each RF observation window is converted into a power spectral density (PSD) sequence and further mapped to a frequency-domain Gramian Angular Field (F-GAF) image. Then, the PSD sequence and F-GAF image are used to extract the spectral envelope and heterogeneous two-dimensional frequency-domain features, respectively. Furthermore, an Adaptive Gated Low-rank Bilinear Interaction (AGLRBI) module is designed for the dual-stream features to model second-order interactions and perform adaptive fusion. In this way, the dual-stream representation framework enhances high-accuracy UAV RF signal identification. Experiments on the DroneRF 10-class classification task show that PFG-DSFNet achieves an accuracy of 99.35% and a Macro-F1 score of 0.9934. Ablation experiments further show that the bilinear interaction and adaptive gating components each contribute to the final performance, and their combination achieves the best result. Additional experiments under mixed-SNR conditions and in measured scenarios further support the robustness of the proposed method.