HPFNet: Hierarchical Perception Fusion Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) is a fundamental task in remote sensing-based surveillance and early warning systems. However, extremely small target size, low signal-to-noise ratio, and complex background clutter make reliable detection highly challenging. To address these issues, we propose a Hierarchical Perception Fusion Network (HPFNet) for IRSTD. Specifically, the Patch-Wise Context Feature Extraction module (PCFE) jointly integrates the Patch Nonlocal Block, convolutional blocks and attention mechanism to enable global–local feature extraction and enhancement, thereby strengthening weak target representations. In addition, the Multi-Level Sparse Cross-Fusion module (MSCF) explicitly performs cross-level feature interaction between encoder and decoder representations, enabling effective fusion of low-level spatial details and high-level semantic cues. A dual Top-K sparsification mechanism is adopted to filters’ irrelevant background features, enabling the attention mechanism to focus more on the target region and thereby bolstering the discriminative power of feature representation. Finally, the Efficient Upsampling Module (EUM) combines upsampling with multi-branch dilated convolutions to enhance feature reconstruction and improve localization accuracy. Extensive experiments on publicly available benchmark datasets demonstrate that HPFNet consistently outperforms existing state-of-the-art IRSTD methods.