SCFusion: Spatial-Channel Cross-Frequency Guided Fusion Network for Infrared–Visible Image Object Detection

While infrared–visible image object detection exhibits advantages in complex scenarios, existing methods still suffer from issues such as static frequency-domain modeling, severe cross-modal interference, and insufficient local detail perception. To address these problems, this paper proposes an infrared and visible image object detection based on a spatial-channel cross-frequency guided fusion network. First, we construct a frequency residual selective transformer to realize local inductive bias and a global receptive field for infrared and visible image feature extraction. Furthermore, the spatial-channel frequency fusion mechanism based on the Homogeneous Frequency Refined Block and the Heterogeneous Spatial-Channel Frequency Fusion Block is proposed to achieve modality-consistent feature fusion. Finally, the frequency reconstruction guided decoder selects high-frequency components to sharpen object boundaries. The SCFusion network was evaluated on four public benchmark datasets (VT821, VT1000, VT5000, and VI-RGBT1500), with test sets containing 411, 400, 2500, and 600 pairs of infrared–visible images, respectively. Following the standard training and testing protocol, the network achieved Mean Absolute Error (MAE) scores of 0.025 (VT821), 0.016 (VT1000), 0.023 (VT5000), and 0.020 (VI-RGBT1500).