The automated interpretation of rock structure can improve the efficiency, accuracy, and consistency of the geological risk assessment of tunnel face. Because of the high uncertainties in the geological images as a result of different regional rock types, as well as in-situ conditions (e.g., temperature, humidity, and construction procedure), previous automated methods have limited performance in classification of rock structure of tunnel face during construction. This paper presents a framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks (CNN), namely Inception-ResNet-V2 (IRV2). A prototype recognition system is implemented to classify 5 types of rock structures including mosaic, granular, layered, block, and fragmentation structures. The proposed IRV2 network is trained by over 35,000 out of 42,400 images extracted from over 150 sections of tunnel faces and tested by the remaining 7400 images. Furthermore, different hyperparameters of the CNN model are introduced to optimize the most efficient algorithm parameter. Among all the discussed models, i.e., ResNet-50, ResNet-101, and Inception-v4, Inception-ResNet-V2 exhibits the best performance in terms of various indicators, such as precision, recall, F-score, and testing time per image. Meanwhile, the model trained by a large database can obtain the object features more comprehensively, leading to higher accuracy. Compared with the original image classification method, the sub-image method is closer to the reality considering both the accuracy and the perspective of error divergence. The experimental results reveal that the proposed method is optimal and efficient for automated classification of rock structure using the geological images of the tunnel face.

岩石结构的自动解释可以提高隧道工作面地质风险评估的效率，准确性和一致性。由于不同区域岩石类型以及现场条件（例如温度，湿度和施工程序）导致的地质图像不确定性很高，因此以前的自动方法在隧道岩石结构分类中的性能有限。施工期间面对。本文提出了使用卷积神经网络（Inception-ResNet-V2，IRV2）基于隧道工作面的地质图像对多种岩石结构进行分类的框架。实施原型识别系统以对5种类型的岩石结构进行分类，包括镶嵌，颗粒，分层，块状和碎片结构。拟议的IRV2网络接受了从150多个隧道工作面中提取的42400张图像中的35,000张以上的训练，并通过其余7400张图像进行了测试。此外，引入了CNN模型的不同超参数以优化最有效的算法参数。在所有讨论的模型（即ResNet-50，ResNet-101和Inception-v4）中，Inception-ResNet-V2在各种指标（例如精度，召回率，F分数和每项测试时间）方面均表现出最佳性能。图片。同时，由大型数据库训练的模型可以更全面地获取对象特征，从而获得更高的准确性。与原始图像分类方法相比，子图像方法从准确性和误差发散角度考虑都更接近实际。