A U-Net-Based Hybrid Network with Large-Kernel Convolution and Spatially Reduced Self-Attention for Structural Plane Segmentation in Tunnel-Face Images

The structural planes exposed on tunnel faces reflect the integrity and geological conditions of surrounding rock, and their accurate segmentation is important for geological logging and surrounding rock assessment. However, these targets are typically slender, discontinuous, and low-contrast, and are easily confused with dust, shadows, water seepage reflections, and blasting-induced textures. To address these challenges, this study proposes a hybrid network based on a U-shaped architecture (U-Net) for structural plane segmentation in tunnel-face images. The network combines a large-kernel detail enhancement module for preserving weak boundaries and fine linear features, a global dependency modeling module based on spatially reduced self-attention for capturing long-range relationships among discontinuous structural plane segments, and a semantic-guided filtering module for suppressing background interference in skip connections. Comparative and ablation experiments were conducted on a self-built tunnel-face structural plane image dataset containing 600 images. The results show that the proposed method achieves Dice, intersection over union (IoU), Precision, and Recall values of 69.28%, 53.00%, 68.92%, and 69.64%, respectively. Compared with the baseline U-Net, the Dice and IoU scores are improved by 6.70 and 7.49 percentage points, respectively. These results demonstrate that the proposed model achieves a favorable balance among local detail preservation, global connectivity modeling, and complex background suppression. It provides a reliable foundation for structural plane parameter extraction, surrounding rock assessment, and intelligent geological logging.