WCDMF-Net: Wavelet-Based Cross-Domain Multistage Feature Fusion Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) plays a critical role in aerospace surveillance and remote sensing systems, yet remains challenging due to low signal-to-noise ratios and complex background interference. While conventional approaches suffer from manual parameter sensitivity causing high false-alarm rates, existing deep learning-based methods exhibit limited performance due to their reliance on spatial features alone, which compromises semantic specificity and localization precision. To overcome these limitations, we propose a wavelet-based cross-domain multistage feature fusion network (WCDMF-Net) that synergizes frequency-domain analysis with spatial-contextual learning. Specifically, we construct a parallel spatial-frequency dual-stream architecture that incorporates Haar wavelet transform and inverse discrete wavelet transform for multiscale frequency feature extraction and reconstruction. Afterwards, we design an adaptive spectral-spatial fusion block that first enhances features within the spatial and spectral domains and then adaptively fuses these refined representations to leverage the complementary strengths of both domains. We further design a multi-scale feature aggregation module (MFAM) to aggregate multi-scale output feature maps in the decoding phase. Finally, a multi-level cross-entropy loss function to achieve accurate target localization and mask prediction. Extensive experiments on NUDT-SIRST, NUAA-SIRST and IRSTD-1k datasets demonstrate the effectiveness of our WCDMF-Net, compared with other state-of-the-art methods.