Think Locally and 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 (WT). 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 multiscale 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.