Infrared small target detection is a critical task in remote sensing. However, it remains highly challenging due to low contrast, heavy background clutter, and large variations in target scale. Traditional convolutional networks are inadequate for joint modeling, as they cannot effectively capture both fine structural details and global contextual dependencies. To address these issues, we propose FSGPNet, a frequency–spatial domain jointly guided perceptual network that explicitly exploits complementary representations in both the frequency and spatial domains. Specifically, a Frequency–Spatial Enhancement Module (FSEM) is introduced to strengthen target details while suppressing background interference through high-frequency enhancement and Perona–Malik diffusion. To enhance global context modeling, we propose a Multi-Scale Global Perception (MSGP) module that integrates non-local attention with multi-scale dilated convolutions, enabling robust background modeling. Furthermore, a Gabor Transformer Attention Module (GTAM) is designed to achieve selective frequency–spatial feature aggregation via self-attention over multi-directional and multi-scale Gabor responses, effectively highlighting discriminative structures of various small targets. Extensive experiments are conducted on two benchmark datasets (IRSTD-1K and NUDT-SIRST) that cover typical remote sensing infrared scenarios. Quantitative and qualitative results demonstrate that FSGPNet consistently outperforms state-of-the-art methods across multiple evaluation metrics. These findings validate the effectiveness and robustness of the proposed FSGPNet for detecting small infrared targets in remote sensing applications.

Infrared small target detection is a critical task in remote sensing, where targets typically span only a few pixels and are easily submerged by complex background clutter. Traditional convolutional networks predominantly rely on data-driven feature learning or computationally expensive global attention mechanisms. In this paper, we propose the Anomaly-Driven Gated Fusion Network (ADGFNet), a lightweight end-to-end framework that explicitly embeds the statistical characteristics into the feature extraction and fusion pipeline. The core of ADGFNet comprises two synergistic modules: Local Anomaly Block (LAB), which decomposes anomaly perception into a Local Contrast Branch capturing multi-scale intensity saliency and a Gradient Prior Branch extracting boundary discontinuities via Sobel operators; and Anomaly-Guided Feature Pyramid Neck (AG-FPN), which reuses the spatial anomaly maps generated by the LAB as guidance gates to perform spatially selective cross-scale feature fusion without requiring additional learnable spatial-attention modules. Comprehensive evaluations on the NUDT-SIRST and IRSTD-1K benchmarks demonstrate that ADGFNet achieves a superior accuracy–efficiency trade-off, attaining the best nIoU and the lowest false alarm rates with only 0.58 M parameters. With TensorRT FP16 acceleration, ADGFNet and its ultra-lightweight variant, ADGFNet-Lite (0.12 M parameters), enable real-time inference without appreciable loss of detection accuracy on an NVIDIA Jetson AGX Xavier.