Think Locally, Act Globally: A Frequency-Spatial Fusion Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) remains challenging due to the extremely low signal-to-noise ratio (SNR). Existing methods struggle to balance accuracy and speed, especially under limited computational resources. To address these issues, we propose the frequency-spatial contextual fusion network (FSCFNet) based on You Only Look Once (YOLO) v10n architecture. Particularly, the novel frequency-spatial convolution (FSConv) is designed that decomposes input features via Haar Wavelet Transform. High-frequency cues focus on local details to highlight small targets, while low-frequency cues provide global information to complement spatial features. Subsequently, the asymmetric cross-domain attention (ACA) is developed to enhance the local central feature extraction, which reflects the typical spatial Gaussian pattern of small targets. Furthermore, we introduce the customized multi-scale receptive contextual Block (MRCB) to capture the long-range information by leveraging diverse dilated convolutions. In addition, the Wasserstein Distance Loss (WDL) is utilized to improve bounding box quality. Extensive experiments on three public datasets including IRSTD-1k, NUDT-SIRST, and NUAA-SIRST confirm the effectiveness of FSCFNet. Notably, FSCFNet surpasses the baseline by 4.7% in precision, 3.3% in recall, and 3.9% in AP@50 on IRSTD-1k, with only a 3.6% increase in parameters. FSCFNet provides a robust solution for real-time infrared surveillance systems under resource-constrained environments. More comparisons are shown in Fig. 1.