针对深埋隧道工程中钻孔图像结构面识别面临的形态多样性、跨尺度特征耦合及复杂噪声干扰等难题，提出一种基于改进U-Net的深度学习模型，实现结构面自动语义分割与几何参数定量表征。针对传统卷积网络固定感受野的局限性，在编码器路径中引入动态蛇形卷积（DSConv），通过自适应变形采样增强对不规则结构面的几何特征提取能力；在解码器路径融合高效通道注意力（ECA）机制，提升模型对关键通道特征的敏感度。基于自主构建的钻孔图像数据集（含770张图像），采用2阶段训练策略优化模型。试验结果表明，模型在验证集上的平均交并比达68.44%，总体像素准确率达93.94%，结构面类别的分割精确度为86.12%。进一步结合正弦曲线拟合算法，从分割结果中自动提取结构面倾向、倾角及宽度参数，其计算结果与人工测量值的平均误差小于2%。本研究通过深度学习与几何建模的融合，显著提升了复杂地质条件下结构面识别的精度与效率，为岩体稳定性定量分析提供了可靠的技术支撑，推动了地质勘察从经验判断向智能化的范式转变。
To address the challenges of morphological diversity, multi-scale feature coupling, and complex noise interference in structural plane recognition from borehole images during deep-buried tunnel engineering, the authors propose an enhanced U-Net-based deep learning model for automated semantic segmentation and quantitative geometric characterization of structural planes. Targeting the limitations of fixed receptive fields in conventional convolutional networks, a dynamic snake convolution module is integrated into the encoder path to enhance geometric feature extraction capability for irregular structural planes through adaptive deformable sampling. The decoder path incorporates an efficient channel attention mechanism to improve sensitivity to critical channel features. Utilizing a selfconstructed borehole image dataset (770 images) with a two-phase training strategy, experimental results demonstrate that the model achieves a mean intersection over union of 68.44%, pixel accuracy of 93.94%, and segmentation precision of 86.12% for structural planes on the validation set. Furthermore, a sinusoidal curve fitting algorithm is implemented to automatically extract orientation, dip angle, and width parameters from segmentation results, yielding an average error below 2% compared to manual measurements. By synergizing deep learning with geometric modeling, the proposed approach remarkably enhances the accuracy and efficiency of structural plane recognition under complex geological conditions, providing reliable technical support for quantitative rock mass stability analysis, and further promoting the paradigm shift in geological exploration from empirical judgment to intelligent analysis.
