Anomaly-Driven Gated Fusion Network for Infrared Small Target Detection

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.