围岩结构面是影响TBM掘进效率与安全性的关键地质因素之一，研发快速、准确、环境适应性强的TBM隧道围岩结构面识别方法具有重要意义。本文提出了一种基于多目视觉摄像与图像识别技术的围岩结构面识别方法。具体而言，以双目相机为方法的设备基础，通过固定机位拍摄大面积裸露围岩的彩色与深度图像，根据双目相机位置与拍摄角度等位置参数，修正深度图像以克服图像畸变问题。进一步采用精细边界刻画(CED)方法，实现了对围岩结构面的精细识别。该方法在传统的卷积神经网络基础上，增设了图像数据的正向传播与反向细化双路径，在反向细化路径中不断强化对图像局部边界的捕捉，捕捉图像中结构面与常规围岩像素的差异，进而刻画围岩结构面边界。依托青岛地铁6号线TBM施工隧道，采集427组现场围岩的彩色与深度图像，通过对比模型识别与结构面实际形态，进一步验证了本方法。

The structural plane of surrounding rock is one of the key geological factors affecting the efficiency and safety of TBM excavation. Developing a fast, accurate, and environmentally adaptable method for identifying the structural plane of surrounding rock in TBM tunnels is of great significance. This article proposes a method for identifying rock structure planes based on multi camera vision and image recognition technology. Specifically, based on the equipment using binocular cameras, color and depth images of large exposed surrounding rocks are captured at fixed positions. The depth images are corrected to overcome image distortion issues based on positional parameters such as the position and shooting angle of the binocular camera. Furthermore, the Fine Boundary Description (CED) method was adopted to achieve precise identification of the structural planes of the surrounding rock. This method is based on traditional convolutional neural networks and adds a dual path of forward propagation and backward refinement of image data. In the backward refinement path, it continuously strengthens the capture of local boundaries in the image, captures the differences between structural planes and conventional surrounding rock pixels in the image, and then characterizes the boundaries of surrounding rock structural planes. Based on the TBM construction tunnel of Qingdao Metro Line 6, 427 sets of color and depth images of the surrounding rock were collected on site. By comparing the model recognition with the actual morphology of cracks, this method was further validated.