DIFTransNet: Dual-Branch Interactive Fusion Network With CNN and Multiscale Transformer for Infrared Small Target Detection

Recent advances in hybrid architectures combining convolutional neural networks (CNNs) and Transformers have demonstrated significant potential in infrared small target detection. However, existing methods suffer from limited interaction depth, leading to weak cross-layer feature coordination. To address this, we propose the DIFTransNet, a dual-branch interactive fusion network that implements hierarchical mutual refinement between CNN and Transformer pathways. Specifically, the DIFTransNet incorporates an interactive fusion module (IFM) at each hybrid encoding stage that bridges local details and global semantics through shared latent space projection and cross-attention mechanisms, enabling aligned deep fusion. In the Transformer branch, we introduce the hierarchical decoupled sparse attention (HDSA) with resolution-progressive window partitioning, which preserves dim targets through dense local sampling while suppressing noise via cross-region sparse correlations. Moreover, rethinking the information gap in conventional skip connections between shallow and deep layers, we propose a reorganized skip augmentation module (RSAM). It introduces a cross-level bidirectional fusion strategy, where the closed-loop architecture enables dual compensation between spatial details and semantic contexts. The experimental results on the NUDT-SIRST, SIRST, and IRSTD-1K datasets demonstrate that the proposed DIFTransNet surpasses current state-of-the-art methods in accurate detection of infrared small targets.