Abstract In this paper, an advanced integrated pixel-level method based on the deep convolutional neural network (DCNN) approach named DeepLabv3+ is proposed for weak interlayers detection and quantification. Furthermore, a database containing 32,040 images of limestone, dolomite, loess clay, and red clay is established to verify this method. The proposed model is then trained, validated, and tested via feeding multiple weak interlayers. Moreover, robustness and adaptability of the proposed model are evaluated, and the weak interlayers are extracted. Compared with the fully convolutional network (FCN)-based method and traditional image techniques, the proposed model provides higher accuracy in terms of boundary recognition. Besides, it can further detect multiple weak interlayers at the pixel level in practice. The results reveal that the proposed model can efficiently segment damage for rock tunnel faces, eliminate more noises, and consequently provide a much faster running speed.

中文翻译：

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基于图像的岩石隧道掌子面弱夹层深度学习分割与量化

摘要 在本文中，提出了一种名为 DeepLabv3+ 的基于深度卷积神经网络 (DCNN) 方法的高级集成像素级方法，用于弱夹层检测和量化。此外，还建立了一个包含 32,040 张石灰岩、白云岩、黄土和红粘土图像的数据库来验证该方法。然后通过提供多个弱中间层来训练、验证和测试所提出的模型。此外，还评估了所提出模型的鲁棒性和适应性，并提取了弱中间层。与基于全卷积网络（FCN）的方法和传统图像技术相比，所提出的模型在边界识别方面提供了更高的准确性。此外，它在实践中还可以进一步检测像素级的多个弱夹层。