For real-time classification of rock-masses in hard-rock tunnels, quick determination of the rock lithology on the tunnel face during construction is essential. Motivated by current breakthroughs in artificial intelligence technology in machine vision, a new automatic detection approach for classifying tunnel lithology based on tunnel face images was developed. The method benefits from residual learning for training a deep convolutional neural network (DCNN), and a multi-scale dilated convolutional attention block is proposed. The block with different dilation rates can provide various receptive fields, and thus it can extract multi-scale features. Moreover, the attention mechanism is utilized to select the salient features adaptively and further improve the performance of the model. In this study, an initial image data set made up of photographs of tunnel faces consisting of basalt, granite, siltstone, and tuff was first collected. After classifying and enhancing the training, validation, and testing data sets, a new image data set was generated. A comparison of the experimental findings demonstrated that the suggested approach outperforms previous classifiers in terms of various indicators, including accuracy, precision, recall, F1-score, and computing time. Finally, a visualization analysis was performed to explain the process of the network in the classification of tunnel lithology through feature extraction. Overall, this study demonstrates the potential of using artificial intelligence methods for in situ rock lithology classification utilizing geological images of the tunnel face.

为了对硬岩隧道中的岩体进行实时分类，施工过程中快速确定掌子面岩石岩性至关重要。在当前机器视觉人工智能技术突破的推动下，开发了一种基于隧道掌子面图像的隧道岩性分类自动检测方法。该方法受益于用于训练深度卷积神经网络（DCNN）的残差学习，并提出了多尺度扩张卷积注意块。不同膨胀率的块可以提供不同的感受野，从而可以提取多尺度特征。此外，利用注意力机制自适应地选择显着特征，进一步提高模型的性能。在这项研究中，首先收集了由玄武岩、花岗岩、粉砂岩和凝灰岩组成的隧道面照片组成的初始图像数据集。对训练、验证和测试数据集进行分类和增强后，生成了新的图像数据集。实验结果的比较表明，所提出的方法在各种指标上都优于以前的分类器，包括准确度、精确度、召回率、F1 分数和计算时间。最后进行可视化分析，通过特征提取来解释网络在隧道岩性分类中的过程。总体而言，这项研究证明了利用人工智能方法利用隧道掌子面地质图像进行原位岩石岩性分类的潜力。