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.