FAA-Net: A Frequency-Aware Attention Network for Single-Frame Infrared Small Target Detection

Infrared small target detection (IRSTD) involves accurately locating the positions of small and dim targets in infrared images and serves as a fundamental component in modern sensing and measurement systems, such as maritime vessel search and airborne monitoring. In the spatial domain, background clutter and sensor-induced noise exhibit similar visual characteristics to true targets, leading to false alarms and missed detections. Existing detection methods primarily focus on extracting local spatial features, while overlooking the differences between targets and background in the frequency domain. To address this limitation, we propose a Frequency-Aware Attention Network (FAA-Net) for IRSTD. Specifically, we design a Frequency-Aware Attention Module (FAM) based on discrete cosine transform to capture informative high-frequency components and enhance local contrast. Furthermore, we construct a Transformer and Convolution Fusion Block (TCFB) to combine the global modeling ability of Transformers with the local feature extraction strength of convolutional layers. By applying FAMs and TCFBs to both shallow and deep layers, FAA-Net captures multi-scale semantic and structural features to generate robust target representations. From a measurement perspective, FAA-Net provides reliable pixel-level outputs that correspond to measurable physical quantities, such as target location and area. We conduct experiments on three publicly available datasets, including NUDT-SIRST, NUAA-SIRST, and IRSTD-1K. Experimental results demonstrate that our presented FAA-Net achieves superior performance compared to existing deep learning-based methods in IRSTD. The source code is available at https://github.com/Dr-Zhuang/FAA-Net-IRSTD.