Journal of Northeastern University(Natural Science) ›› 2024, Vol. 45 ›› Issue (1): 101-110.DOI: 10.12068/j.issn.1005-3026.2024.01.013
• Resources & Civil Engineering • Previous Articles Next Articles
Hao SUN1,2, Zong-sheng DAI1,2, Ai-bing JIN1,2, Yan CHEN3
Received:
2022-08-16
Online:
2024-01-15
Published:
2024-04-02
CLC Number:
Hao SUN, Zong-sheng DAI, Ai-bing JIN, Yan CHEN. Intelligent Identification and Parameter Extraction of Key Joints in Rock (Mass) Based on AttentionR2U-net[J]. Journal of Northeastern University(Natural Science), 2024, 45(1): 101-110.
名称 | 参数 | 名称 | 参数 |
---|---|---|---|
CPU | Intel i5-11400H | GPU | NVIDIA Geforce RTX 3050 |
系统 | Windows 10 | 语言 | Python 3.6 |
RAM | 16 GB | 框架 | Tensorflow和Keras |
Cuda | 11.1 |
Table 1 Computing parameters of AttentionR2U‐net network
名称 | 参数 | 名称 | 参数 |
---|---|---|---|
CPU | Intel i5-11400H | GPU | NVIDIA Geforce RTX 3050 |
系统 | Windows 10 | 语言 | Python 3.6 |
RAM | 16 GB | 框架 | Tensorflow和Keras |
Cuda | 11.1 |
指标 | 含义及作用 | 公式 |
---|---|---|
二分类交叉损失函数 | 标签图像和预测图像相似程度;用于评估二分类问题中网络分割结果与给定标准结果的相似程度,也可运用反向传播算法对网络的参数进行优化. | |
准确率(Accuracy,Acc) | 正确预测出节理和背景的像素占总像素的百分比;反映分类器的分类效果. | |
查准率(Precision,P) | 预测像素中真实的节理像素占所有预测为节理像素的百分比;反映被预测为节理的结果中正确的概率. | |
查全率(Recall,R) | 预测像素中真实的节理像素占所有真实节理像素百分比;反映节理被预测出的概率. | |
灵敏度(Sen) | 数值上等于R值,值越大,假负例越少;体现模型对节理的敏感程度. | |
特异性(Spe) | 预测出的背景像素占真实背景像素的百分比;体现模型判别背景的能力. | |
Dice相似系数(Dice) | 标签图像和预测图像的相似度程度;更直观地反映模型的优劣程度. |
Table 2 Model superiority evaluation index
指标 | 含义及作用 | 公式 |
---|---|---|
二分类交叉损失函数 | 标签图像和预测图像相似程度;用于评估二分类问题中网络分割结果与给定标准结果的相似程度,也可运用反向传播算法对网络的参数进行优化. | |
准确率(Accuracy,Acc) | 正确预测出节理和背景的像素占总像素的百分比;反映分类器的分类效果. | |
查准率(Precision,P) | 预测像素中真实的节理像素占所有预测为节理像素的百分比;反映被预测为节理的结果中正确的概率. | |
查全率(Recall,R) | 预测像素中真实的节理像素占所有真实节理像素百分比;反映节理被预测出的概率. | |
灵敏度(Sen) | 数值上等于R值,值越大,假负例越少;体现模型对节理的敏感程度. | |
特异性(Spe) | 预测出的背景像素占真实背景像素的百分比;体现模型判别背景的能力. | |
Dice相似系数(Dice) | 标签图像和预测图像的相似度程度;更直观地反映模型的优劣程度. |
指标 | 样品1 | 样品2 | 样品3 | 样品4 |
---|---|---|---|---|
TP | 4022 | 10921 | 3923 | 4570 |
TN | 58404 | 51487 | 58501 | 57857 |
FN | 19 | 69 | 11 | 34 |
FP | 55 | 23 | 65 | 38 |
Sen | 0.995 | 0.994 | 0.997 | 0.992 |
Spe | 0.999 | 0.999 | 0.998 | 0.999 |
P | 0.986 | 0.998 | 0.984 | 0.992 |
Dice | 0.991 | 0.996 | 0.990 | 0.992 |
Table 3 Quantitative results of predicting different joint images based on AttentionR2U‐net
指标 | 样品1 | 样品2 | 样品3 | 样品4 |
---|---|---|---|---|
TP | 4022 | 10921 | 3923 | 4570 |
TN | 58404 | 51487 | 58501 | 57857 |
FN | 19 | 69 | 11 | 34 |
FP | 55 | 23 | 65 | 38 |
Sen | 0.995 | 0.994 | 0.997 | 0.992 |
Spe | 0.999 | 0.999 | 0.998 | 0.999 |
P | 0.986 | 0.998 | 0.984 | 0.992 |
Dice | 0.991 | 0.996 | 0.990 | 0.992 |
指标 | Acc | Sen | Spe | P | Dice |
---|---|---|---|---|---|
平均 | 0.997 | 0.991 | 0.998 | 0.990 | 0.990 |
最高 | 0.999 | 0.998 | 0.999 | 0.998 | 0.997 |
Table 4 Calculation results of each index in the test set
指标 | Acc | Sen | Spe | P | Dice |
---|---|---|---|---|---|
平均 | 0.997 | 0.991 | 0.998 | 0.990 | 0.990 |
最高 | 0.999 | 0.998 | 0.999 | 0.998 | 0.997 |
算法 | 混凝土 | 大理岩 | 花岗岩 | 砂岩 | 龟裂土 |
---|---|---|---|---|---|
本文算法 | 0.985 | 0.953 | 0.974 | 0.983 | 0.963 |
大津法 | 0.879 | 0.867 | 0.375 | 0.258 | 0.785 |
边缘检测法 | 0.263 | 0.341 | 0.185 | 0.157 | 0.438 |
区域生长法 | 0.846 | 0.367 | 0.735 | 0.786 | 0.579 |
血管增强法 | 0.564 | 0.459 | 0.708 | 0.723 | 0.738 |
Table 5 Diceresults for fracture identification in other fields
算法 | 混凝土 | 大理岩 | 花岗岩 | 砂岩 | 龟裂土 |
---|---|---|---|---|---|
本文算法 | 0.985 | 0.953 | 0.974 | 0.983 | 0.963 |
大津法 | 0.879 | 0.867 | 0.375 | 0.258 | 0.785 |
边缘检测法 | 0.263 | 0.341 | 0.185 | 0.157 | 0.438 |
区域生长法 | 0.846 | 0.367 | 0.735 | 0.786 | 0.579 |
血管增强法 | 0.564 | 0.459 | 0.708 | 0.723 | 0.738 |
方法 | 关键节理 识别率 | 关键节理精度 | 单条节理精度 |
---|---|---|---|
原模型 | 0.568 | 0.842 | 0.990 |
方法1 | 0.738 | 0.916 | 0.953 |
方法2 | 1.000 | 0.990 | 0.990 |
方法3 | 1.000 | 0.953 | 0.953 |
Table 6 Comparison of results of two coupling methods for critical joint identification
方法 | 关键节理 识别率 | 关键节理精度 | 单条节理精度 |
---|---|---|---|
原模型 | 0.568 | 0.842 | 0.990 |
方法1 | 0.738 | 0.916 | 0.953 |
方法2 | 1.000 | 0.990 | 0.990 |
方法3 | 1.000 | 0.953 | 0.953 |
1 | 黄润秋.20世纪以来中国的大型滑坡及其发生机制[J].岩石力学与工程学报,2007,26(3):433-454. |
Huang Run‐qiu.Large‐scale landslides and their sliding mechanisms in China since the 20th century[J].Chinese Journal of Rock Mechanics and Engineering,2007,26(3):433-454. | |
2 | Chen S W, Walske M L, Davies I J.Rapid mapping and analysing rock mass discontinuities with 3D terrestrial laser scanning in the underground excavation[J].International Journal of Rock Mechanics and Mining Sciences,2018,110:28-35. |
3 | Ismail A, Ahmad‐Safuan A R, Sa'ari R,et al.Application of combined terrestrial laser scanning and unmanned aerial vehicle digital photogrammetry method in high rock slope stability analysis:a case study[J].Measurement,2022,195:111161. |
4 | Agar Ozbek A S, Weerheijm J, van Breugel K.High speed photography technique for measuring impact strength of porous concrete[J].Construction and Building Materials,2018,186:1092-1104. |
5 | Sturzenegger M, Stead D.Close‐range terrestrial digital photogrammetry and terrestrial laser scanning for discontinuity characterization on rock cuts[J].Engineering Geology,2009,106(3/4):163-182. |
6 | 金爱兵,陈帅军,赵安宇,等.基于无人机摄影测量的露天矿边坡数值模拟[J].岩土力学,2021,42(1):255-264. |
Jin Ai‐bing, Chen Shuai‐jun, Zhao An‐yu,et al.Numerical simulation of open‐pit mine slope based on unmanned aerial vehicle photogrammetry[J].Rock and Soil Mechanics,2021,42(1):255-264. | |
7 | 贾曙光,金爱兵,赵怡晴.无人机摄影测量在高陡边坡地质调查中的应用[J].岩土力学,2018,39(3):1130-1136. |
Jia Shu‐guang, Jin Ai‐bing, Zhao Yi‐qing.Application of UAV oblique photogrammetry in the field of geology survey at the high and steep slope[J].Rock and Soil Mechanics,2018,39(3):1130-1136. | |
8 | Talab A M A, Huang Z C, Xi F,et al.Detection crack in image using Otsu method and multiple filtering in image processing techniques[J].Optik,2016,127(3):1030-1033. |
9 | Kheradmandi N, Mehranfar V.A critical review and comparative study on image segmentation‐based techniques for pavement crack detection[J].Construction and Building Materials,2022,321:126162. |
10 | Nhat‐Duc H, Nguyen Q L, Tran V D.Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network[J].Automation in Construction,2018,94:203-213. |
11 | Wang L T, Gu X Y, Liu Z,et al.Automatic detection of asphalt pavement thickness:a method combining GPR images and improved Canny algorithm[J].Measurement,2022,196:111248. |
12 | Kang C C, Wang W J, Kang C H.Image segmentation with complicated background by using seeded region growing[J].AEU-International Journal of Electronics and Communications,2012,66(9):767-771. |
13 | Tang Y D, He L, Lu W,et al.A novel approach for fracture skeleton extraction from rock surface images[J].International Journal of Rock Mechanics and Mining Sciences,2021,142:104732. |
14 | Zhu J S, Song J B.Weakly supervised network based intelligent identification of cracks in asphalt concrete bridge deck[J].Alexandria Engineering Journal,2020,59(3):1307-1317. |
15 | Cui X N, Wang Q C, Dai J P,et al.Pixel‐level intelligent recognition of concrete cracks based on DRACNN[J].Materials Letters,2022,306:130867. |
16 | 柳厚祥,李汪石,查焕奕,等.基于深度学习技术的公路隧道围岩分级方法[J].岩土工程学报,2018,40(10):1809-1817. |
Liu Hou‐xiang, Li Wang‐shi, Zha Huan‐yi,et al.Method for surrounding rock mass classification of highway tunnels based on deep learning technology[J].Chinese Journal of Geotechnical Engineering,2018,40(10):1809-1817. | |
17 | 梁世豪.基于深度学习的野外露头区岩石裂缝识别方法研究[D].大庆:东北石油大学,2020. |
Liang Shi‐hao.Research on identification method of rock fractures in outcrop area based on deep learning[D].Daqing:Northeast Petroleum University,2020. | |
18 | Chaiyasarn K, Buatik A, Mohamad H,et al.Integrated pixel‐level CNN‐FCN crack detection via photogrammetric 3D texture mapping of concrete structures[J].Automation in Construction,2022,140:104388. |
19 | Chen J Y, Zhou M L, Huang H W,et al.Automated extraction and evaluation of fracture trace maps from rock tunnel face images via deep learning[J].International Journal of Rock Mechanics and Mining Sciences,2021,142:104745. |
20 | 张紫杉,王述红,王鹏宇,等.岩坡坡面裂隙网络智能识别与参数提取[J].岩土工程学报,2021,43(12):2240-2248. |
Zhang Zi‐shan, Wang Shu‐hong, Wang Peng‐yu,et al.Intelligent identification and extraction of geometric parameters for surface fracture networks of rocky slopes[J].Chinese Journal of Geotechnical Engineering,2021,43(12):2240-2248. | |
21 | Xie Z Y, Asari V K, Haritashya U K.Evaluating deep‐learning models for debris‐covered glacier mapping[J].Applied Computing and Geosciences,2021,12:100071. |
22 | Obeso A M, Benois‐Pineau J, García Vázquez M S,et al.Visual vs internal attention mechanisms in deep neural networks for image classification and object detection[J].Pattern Recognition,2022,123:108411. |
23 | 于文玲,刘波,刘华,等.基于Attention Gates和R2U‐net的遥感影像建筑物提取方法[J].地理与地理信息科学,2022,38(3):31-36,42. |
Yu Wen‐ling, Liu Bo, Liu Hua,et al.Building extraction from remote sensing images based on the R2U‐net model and attention gates[J].Geography and Geo-Information Science,2022,38(3):31-36,42. | |
24 | Turkan Y, Hong J, Laflamme S,et al.Adaptive wavelet neural network for terrestrial laser scanner‐based crack detection[J].Automation in Construction,2018,94:191-202. |
25 | Zheng M J, Lei Z J, Zhang K.Intelligent detection of building cracks based on deep learning[J].Image and Vision Computing,2020,103:103987. |
26 | Zhao Y X, Sun B, Liu S M,et al.Identification of mining induced ground fissures using UAV and infrared thermal imager:temperature variation and fissure evolution[J].ISPRS Journal of Photogrammetry and Remote Sensing,2021,180:45-64. |
27 | Tang C A, Webb A A G, Moore W B,et al.Breaking earth’s shell into a global plate network[J].Nature Communications,2020,11:3621. |
28 | Shi Y, Cui L M, Qi Z Q,et al.Automatic road crack detection using random structured forests[J].IEEE Transactions on Intelligent Transportation Systems,2016,17(12):3434-3445. |
29 | 金爱兵,陆通,王本鑫,等.基于力学等效的岩体关键节理迹长阈值研究[J].岩石力学与工程学报,2022,41(5):904-915. |
Jin Ai‐bing, Lu Tong, Wang Ben‐xin,et al.Study on the threshold of key joint trace length in rock mass based on mechanical equivalence[J].Chinese Journal of Rock Mechanics and Engineering,2022,41(5):904-915. | |
30 | Domínguez C, Heras J, Pascual V.IJ‐OpenCV:combining ImageJ and OpenCV for processing images in biomedicine[J].Computers in Biology and Medicine,2017,84:189-194. |
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