Infrared small target detection (IRSTD) focuses on the accurate localization of small, low-contrast targets under cluttered background conditions, with wide-ranging deployments in practice, particularly for maritime rescue operations and traffic surveillance systems. However, this task still faces multiple challenges, including vulnerability to background noise, poor adaptability to multi-scale features, and limited feature representation capacity. In response to these challenges, we present HFCNet, a multi-scale detection network that leverages collaborative modeling in both spatial and frequency domains. We design a Hessian-based feature extraction branch (HB) to capture coarse-grained structural textures of the target, followed by multi-stage feature fusion to enhance fine-grained details. Meanwhile, a Local Frequency Representation Module (LFRM) is introduced to decouple the target’s energy and spatial information in the frequency domain. This representation is further constrained by a Frequency-Domain Loss (FD) to align predicted features with ground truth in the frequency space. To further enhance performance, we integrate the Residual Coordinate Attention Block (RCB), which adaptively refines multi-scale feature representations with spatial awareness, ultimately boosting the network’s sensitivity to small and weak targets. Experimental evaluations demonstrate that the proposed HFCNet consistently surpasses leading state-of-the-art techniques when tested on the public IRSTD-1k and NUDT-SIRST datasets.