四类常见边坡岩石类别识别和边界范围确定的方法

王鹏宇; 王述红

doi:10.11779/CJGE201908015

四类常见边坡岩石类别识别和边界范围确定的方法 English Version

王鹏宇,
王述红^,

东北大学资源与土木工程学院,辽宁沈阳 110819

基金项目: 国家自然科学基金项目（U1602232,51474050）; 中央高校基本科研业务专项资金项目（N170108029）; 辽宁省自然科学基金项目（20170540304; 20170520341）; 东北大学双一流建设项目（2018）

详细信息

作者简介:
王鹏宇(1994— ),男,博士研究生,主要从事岩石力学方面的研究工作。E-mail：wangpengyu6666@126.com。

通讯作者:
王述红，E-mail：shwang@mail.neu.edu.cn

中图分类号: TU45
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出版历程
- 收稿日期: 2019-01-21
- 发布日期: 2019-08-24

Method for identifying four common rock types of slopes and determining boundary range

WANG Peng-yu,
WANG Shu-hong^,

School of Resources & Civil Engineering, Northeastern University, Shenyang 110819, China

摘要

摘要: 岩质边坡岩石的分类与边界范围的确定对于边坡稳定性的分析至关重要,目前人工方法效率低且受主观因素影响,所以基于Tensorflow建立了岩质边坡图像集分析的卷积神经网络模型,通过卷积操作和池化操作分别对80000张岩质边坡图像进行特征信息的提取和压缩,然后对网络模型进行训练从而实现了岩质边坡岩石的自动识别与分类;采用训练集和测试集中的岩质边坡图像对模型进行检验分析,训练集准确率达到了98%,测试集准确率达到了90%,显示了训练之后的网络模型具有良好的鲁棒性,达到了理想的训练效果。接下来以边坡不同岩石的颜色为主要区分依据,利用深度学习回归操作对岩质边坡不同种类岩石的范围进行确定,为验证算法效果,选取标准彩色岩质边坡图像进行仿真试验,边界检测效果准确。最终采用深度学习建立的网络模型,实现了岩质边坡岩石识别与边界范围划分的快速化、自动化,为后续将图像识别获取的岩质边坡信息导入团队自主研发的GeoSMA-3D软件中,作为对岩质边坡等级判定的重要参数。
- 岩质边坡 /
- 卷积神经网络 /
- 深度学习 /
- 图像识别 /
- 边界范围
Abstract: Rock classification and boundary determination of rock slopes are very important for the analysis of slope stability. At present, the artificial methods are inefficient and affected by subjective factors. So a convolution neural network model for the image set analysis of a rock slope is established based on Tensorflow. Through convolution operation and pooling operation, the feature information of 8000 original rock slope images is extracted and compressed respectively. Then the network model is trained to realize the automatic recognition and classification of the rock slope. The model is tested and analyzed by using the images of rock slopes in training set and testing set. The accuracy rate of the training set and the testing set is 98% and 90%, respectively. It is shown that the network model after training has good robustness and achieves ideal training effect. Next, the color of different rocks on the slope is taken as the main basis. The boundary of different types of rock on the rock slope is calibrated by the deep learning boundary extraction technology. To verify the effectiveness of the algorithm, the standard color image of the rock slope is selected for simulation experiment, and the results of boundary detection are accurate. The network model established by deep learning realizes the requirements of rapid and automatic rock identification and boundary range division of rock slopes, and introduces the rock slope information acquired by image recognition into the GeoSMA-3D software independently developed by the team,as an important parameter for determining the grade of rock slopes.
- rock slope /
- convolutional neural network /
- deep learning /
- image recognition /
- boundary range

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