DCFD-DETR: Dual-Stream Cascade Frequency Decoupling and Difference Enhancement Transformer for Infrared Small Target Detection

Infrared small target detection faces persistent challenges due to low signal-to-noise ratios (SNR) and the scarcity of texture information in complex backgrounds. Existing deep learning methods are often limited by the “vanishing target” phenomenon in deep layers and the re-introduction of background noise during feature fusion. To address these issues, this article proposes a novel Dual-Stream Cascade Frequency Decoupling and Difference Enhancement Transformer (DCFD-DETR). This framework introduces a parallel dual-stream architecture that deeply integrates frequency-domain physical priors with spatial features through cross-domain semantic interaction and complementary fusion. Specifically, a Wavelet-Based Frequency-Spatial Decoupling Module (WB-FSDM) and a Spatial Purification Module (SPM) are constructed in a cascade manner to explicitly decouple high-frequency target singularities from low-frequency background clutter, utilizing the latter to effectively suppress background interference in the spatial domain. To facilitate efficient cross-domain interaction, a Cross-Guided Hybrid Attention Fusion Block (CG-HAFB) is designed to align semantic features. Furthermore, a Difference-Enhanced Semantic-Gated Fusion (SGF-Diff) module is proposed, employing a “Difference is the Target” mechanism to enhance target salience while filtering out consistent noise. Finally, to alleviate the optimization bottleneck of small target regression, a Dynamic Auxiliary-Box Optimization Strategy is introduced. By establishing a dual-head parallel mechanism with shared query constraints, this strategy accelerates convergence and ensures precise pixel-level localization. Extensive experiments on the NUDT-SIRST and IRSTD- 1K benchmarks demonstrate that DCFD-DETR outperforms existing state-of-the-art methods, exhibiting superior robustness and detection accuracy in complex scenarios.