Hard-rock tunnel lithology identification using multi-scale dilated convolutional attention network based on tunnel face images
Hard-rock tunnel lithology identification using multi-scale dilated convolutional attention network based on tunnel face images
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DOI：
10.1007/s11709-023-0002-1
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发表时间：
2024-02
影响因子：
3
通讯作者：
Wenjun Zhang;Wuqi Zhang;G. Zhang;Jun Huang;Minggeng Li;Xiaohui Wang;Fei Ye;Xiaoming Guan
中科院分区：
文献类型：
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作者：
Wenjun Zhang;Wuqi Zhang;G. Zhang;Jun Huang;Minggeng Li;Xiaohui Wang;Fei Ye;Xiaoming Guan
在硬岩隧道施工过程中，快速确定隧道掌子面的岩石岩性是实现围岩实时分类的关键。针对当前机器视觉领域人工智能技术的突破，提出了一种基于掌子面图像的隧道岩性自动识别方法。该方法受益于用于训练深度卷积神经网络（DCNN）的残差学习，并且提出了多尺度扩张卷积注意力块。具有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著特征，进一步提高了模型的性能。在这项研究中，最初的图像数据集由玄武岩，花岗岩，粉砂岩和凝灰岩组成的隧道工作面的照片。在对训练、验证和测试数据集进行分类和增强之后，生成新的图像数据集。实验结果的比较表明，建议的方法优于以前的分类器在各种指标，包括准确率，精度，召回率，F1分数，和计算时间。最后，通过特征提取，对网络在隧道岩性分类中的应用过程进行了可视化分析。总体而言，这项研究表明，使用人工智能方法的潜力forin siturock岩性分类利用地质图像的隧道工作面。
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 forin siturock lithology classification utilizing geological images of the tunnel face.