Accurate recognition of rock fractures is an important problem in rock engineering because fractures greatly influence the mechanical and hydraulic properties of rock structures. However, existing image segmentation methods for identifying rock fractures tend to be limited to handling only very simple fracture images, despite many real cases containing interfering objects or features such as dark surfaces, stripes (e.g., from foliation), infilling materials, scratches, shadows, and vegetation. Here, we propose a novel deep convolutional neural network to construct the first model that is applicable in the field. After selecting U-Net, a simple and powerful network for semantic segmentation, as a baseline network, we tested network architectures by applying atrous convolutions and extra skip connections to develop an optimal network specialized for rock fracture segmentation. The rate of erroneously detecting non-fracture objects or features was reduced by using the atrous convolution module, and more skip connections were appropriately added to increase the detection rate of the actual fractures. The model's performance gradually improved as these new techniques were added to the original model. Contrast-limited adaptive histogram equalization and a fully connected conditional random field were applied before and after the network, respectively, to enhance the model’s performance. Evaluation of the proposed model using raw images of diverse site conditions shows that it can effectively distinguish rock fractures from various interfering objects and features. The source code and pre-trained model can be freely download from GitHub repository (https://github.com/Montherapy/Rock-fracture-segmentation-with-Tensorflow).

岩石裂缝的准确识别是岩石工程中的一个重要问题，因为裂缝对岩石结构的力学和水力特性影响很大。然而，现有的用于识别岩石裂缝的图像分割方法往往仅限于处理非常简单的裂缝图像，尽管许多真实情况包含干扰对象或特征，例如暗表面、条纹（例如，来自叶理）、填充材料、划痕、阴影, 和植被。在这里，我们提出了一种新颖的深度卷积神经网络来构建第一个适用于该领域的模型。在选择了简单而强大的语义分割网络 U-Net 作为基线网络后，我们通过应用多孔卷积和额外的跳跃连接来测试网络架构，以开发专门用于岩石裂缝分割的最佳网络。使用多孔卷积模块降低了对非断裂物体或特征的错误检测率，并适当增加了更多的跳跃连接，以提高实际断裂的检测率。随着这些新技术被添加到原始模型中，模型的性能逐渐提高。分别在网络前后应用对比度限制自适应直方图均衡和全连接条件随机场，以提高模型的性能。使用不同场地条件的原始图像对所提出的模型进行评估表明，它可以有效地将岩石裂缝与各种干扰物体和特征区分开来。源代码和预训练模型可以从 GitHub 存储库（https://github.com/Montherapy/Rock-fracture-segmentation-with-Tensorflow）免费下载。