Deep learning-based recognition method of red bed soft rock image

被引:2
|
作者
Bin, Yan [1 ]
Lining, Zheng [1 ]
Xin, Wang [1 ]
Qijie, Li [1 ]
机构
[1] China Southwest Geotech Invest & Design Inst Co Lt, Chengdu 610052, Peoples R China
关键词
deep learning; intelligent identification system; intelligent survey; red bed soft rock; IDENTIFICATION;
D O I
10.1002/gj.4752
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
In order to improve the investigation efficiency, improve the traditional engineering investigation work mode, realize the intelligent investigation, and improve the economic and social benefits of enterprises, a red soft rock image intelligent analysis and recognition system is proposed based on deep learning methods. The intelligent recognition system includes two core algorithms: soft rock image decomposition and soft rock lithology/weathering degree recognition. The research shows that the identification model of weathering degree and the lithology identification model based on the convolutional neural network (CNN) algorithm have a good identification effect, and the identification probability of moderately weathered rock reaches 95.22% and the accuracy of lithology identification is 91.34%. With the increase in training data, the recognition effect will be further improved. The intelligent identification system has been integrated into the WeChat mini programme, App, and Web system, which can be directly applied to field geological survey operations to assist geological workers in the investigation work, and realize the intelligent identification and classification of red bed soft rock.
引用
收藏
页码:2418 / 2426
页数:9
相关论文
共 50 条
  • [31] A Deep Learning-Based Electromagnetic Ultrasonic Recognition Method for Surface Roughness of Workpeice
    Cai Z.
    Sun Y.
    Zhao Z.
    Li Y.
    Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2022, 37 (15): : 3743 - 3752
  • [32] Deep Learning-Based Intelligent Image Recognition and Its Applications in Financial Technology Services
    Wang, Qiuwen
    Wang, Pengxiang
    Chang, Yongzhi
    TRAITEMENT DU SIGNAL, 2023, 40 (02) : 735 - 742
  • [33] A Deep Learning-Based Satellite Target Recognition Method Using Radar Data
    Lu, Wang
    Zhang, Yasheng
    Xu, Can
    Lin, Caiyong
    Huo, Yurong
    SENSORS, 2019, 19 (09)
  • [34] A deep learning-based method for aluminium foil-surface defect recognition
    Wang H.
    Gao C.
    Ling Y.
    International Journal of Information and Communication Technology, 2021, 19 (03) : 231 - 241
  • [35] A Deep Learning-Based Animation Video Image Data Anomaly Detection and Recognition Algorithm
    Li, Cheng
    Qian, Qiguang
    JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING, 2024, 36 (01)
  • [36] Research on Image Recognition Method of Class Graph Based on Deep Learning
    Wang, Kai
    Liu, Wei
    Gao, Sheng
    Mu, Yongan
    Xu, Fan
    2023 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE INNOVATION, ICAII 2023, 2023, : 65 - 71
  • [37] Deep learning-based spam image filtering
    Salama, Wessam M.
    Aly, Moustafa H.
    Abouelseoud, Yasmine
    ALEXANDRIA ENGINEERING JOURNAL, 2023, 68 : 461 - 468
  • [38] A Deep Vision Learning-Based Intelligent Recognition Method for Dynamic Sports Gestures
    Xu, Jiao
    Fan, Xingfeng
    JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS, 2024, 33 (07)
  • [39] Real-time deep learning-based image recognition for applications in automated positioning and injection of biological cells
    Sadak, Ferhat
    Saadat, Mozafar
    Hajiyavand, Amir M.
    COMPUTERS IN BIOLOGY AND MEDICINE, 2020, 125
  • [40] Improving Deep Learning-Based Digital Image Correlation with Domain Decomposition Method
    Y. Chi
    Y. Liu
    B. Pan
    Experimental Mechanics, 2024, 64 : 575 - 586