FGC-ConvNeXt: Frequency-Guided Collaborative Modeling of Spatial and Frequency Features for Han Stone Relief Image Classification

Han stone reliefs contain rich historical and artistic information, but their images often exhibit severe weathering, rubbing noise, and complex carving textures, making fine-grained classification challenging. To address the strong coupling between global semantic structures and local material textures, this paper proposes Frequency-Guided Collaborative ConvNeXt (FGC-ConvNeXt), a dual-branch spatial–frequency classification framework. The spatial branch extracts multi-scale semantic features using ConvNeXt-Tiny, while the frequency branch applies Fast Fourier Transform to model complementary structural and texture-sensitive information. A fixed frequency threshold of τ = 0.22 is used for frequency decomposition, and both branches are projected into a 512-dimensional feature space. The Spatial–Frequency Gated Fusion module dynamically adjusts their contributions at the sample level, while frequency-domain auxiliary supervision with a loss weight of λ = 0.4 prevents branch degradation during joint optimization. Experiments were conducted on a self-constructed four-class dataset containing 6368 Han stone relief images with an input resolution of 224 × 224 pixels. Over three independent runs, FGC-ConvNeXt achieved an accuracy of 97.6 ± 0.2%, a precision of 97.8 ± 0.2%, a recall of 97.4 ± 0.3%, and an F1-score of 97.6 ± 0.2%, outperforming the ConvNeXt-Tiny baseline by 4.1 percentage points in accuracy. The proposed model contains 29.8 M parameters. Visualization analyses provide qualitative support for more separated feature distributions and more concentrated activation patterns, while the robustness experiments indicate improved classification stability under the evaluated conditions.