为解决在隧道开挖及施工过程中掌子面岩体结构面几何参数判识存在人工判读主观性强、效率低等问题，利用计算机视觉技术实现结构面判识与几何参数解析。首先，通过改进神经网络模型实现掌子面轮廓和结构面分割；然后，通过2种集成学习方法对5个结构面分割模型进行融合；最后，利用图像处理技术解析得到掌子面岩体的结构面条数、组数和平均间距等几何参数。结果表明： 1）采用U²-Net语义分割模型实现轮廓面分割任务，模型的评价指标精确率p、召回率r、f₁分数分别为0.929、0.926、0.928； 2）采用5个以Swin Transformer模型为框架改进的基模型实现结构面分割，其中，SwinT-Upernet基模型的交并比（I_IoU）和平均交并比（M_IoU）分别达到了66.95%和81.72%，Resnet50-Upernet基模型的平均像素准确率（M_Acc）达到了88.33%；3）加权平均法集成的结构面组数解析准确率达到了82.9%，结构面平均间距解析相对误差为16.15%。

The manual interpretation of structural planes in rock masses during tunnel excavation and construction is highly subjective and inefficient. To address this, computer vision technology is applied to identify structural planes and analyze their geometric parameters. First, an improved neural network model is improved to segment the contours of tunnel faces and structural planes. Next, five base models are integrated using two ensemble learning methods. Finally, image processing techniques are used to analyze the geometric parameters of structural planes, including the number of structural planes, the number of groups, and the average distance. The results indicate the following: (1) The precision, recall, and f₁ scores of the U²-Net semantic segmentation model are 0.929，0.926，and 0.928. (2) Five improved base models, derived from the Swin Transformer model, are utilized for structural plane segmentation. The I_IoU and M_IoU of the SwinT-Upernet base model reach 66.95% and 81.72%, respectively, while the M_Acc of the Resnet50-Upernet base model reaches 88.33%. (3) The accuracy of structural plane group number analysis using the weighted average method is 82.9%, and the relative error in the average distance analysis is 16.15%.