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    基于深度学习的钻孔图像岩体结构面识别

    Detection of Rock Discontinuities in Borehole Images Based on a Deep Learning Method

    • 摘要: 岩体结构面对岩体稳定性和渗透性有着重要影响,是决定深部地下工程稳定性的重要因素.针对井下电视技术获取的钻孔影像,提出了一种基于深度学习算法(You Only Look Once version 4,YOLO v4)的岩体结构面识别方法,并计算识别岩体结构面的几何参数.首先,采集图像数据并进行预处理.以某隧道工程为案例,使用智能钻孔光学成像仪采集4号和6号钻孔图像,筛选含有结构面的钻孔图像进行标注以建立Ground truth数据集.从中随机选择数据集的70%作为训练数据、10%作为验证数据、20%作为测试数据,并对训练数据集使用数据增强处理.接下来使用CSPDarkNet53网络作为特征提取网络,构建YOLO v4模型,并采用试错法获取最优参数进行模型训练.利用测试集生成P-R(Precision-Recall)曲线来测试最终的模型训练效果,结果显示P-R曲线的平均精度达0.87,这表明YOLO v4训练结果较好.最后,将定位的结构面采用Canny算法通过拟合上、中、下正弦曲线函数获取岩体结构面边缘,并依据正弦函数的系数计算结构面的4个几何参数(倾向、倾角、深度和张开度).

       

      Abstract: Rock discontinuity has an important influence on the stability and permeability of the rock mass,thereby significantly determining the stability of deep underground engineering.This paper proposes a method based on a deep learning algorithm (You Only Look Once version 4,YOLO v4) for the identification and positioning of rock discontinuities and the calculation of geometric parameters using borehole images obtained by downhole television technology.First,image data is collected and preprocessed.For the tunnel project,the Intelligent Drilling Optical Imager equipment is used to collect images of borehol No.4 and No.6,and the images were processed to seperate those containing rock discontinuities with annotations to create a Ground truth dataset,with 70% of the dataset randomly selected under data enhancement as training data,10% as verification data and 20% as test data.The CSPDarkNet53 was then used as the feature extraction network to build the YOLO v4 model,with optimal parameters determined through trial and error for model training.The test set was designed to generate the P-R (Precision-Recall) curve to test the final model training effect,and the result shows that the average precision of the P-R curve reaches 0.87,indicating that the training result of YOLO v4 is better.Finally,the Canny algorithm was used to obtain the edges of the rock discontinuities by fitting the upper,middle and lower sine functions to the localised structural surface,allowing the four geometric parameters (dip direction,dip angle,depth and aperture) to be calculated.

       

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