Quick recognition of rock images for mobile applications

被引：1
作者：
Wang C. [1 ]
Li Y. [1 ]
Fan G. [1 ]
Chen F. [1 ]
Wang W. [2 ]
机构：
[1] School of Information Science and Technology, Beijing Forestry University, Beijing
[2] College of Engineering, San Diego State University, San Diego, 92182, CA
来源：
Journal of Engineering Science and Technology Review | 2018年 / 11卷 / 04期
关键词：
Deep learning; Mobile devices; Rock recognition; Transfer learning;
D O I：
10.25103/jestr.114.14
中图分类号：
学科分类号：
摘要：
Rock lithology identification is an important aspect of field geological surveys. However, traditional identification methods cannot obtain real-time effective feedback, a limitation that is ineffective for further implementation of field geological surveys. To obtain rock lithology information quickly in field geological surveys, an automatic identification method of rock lithology applicable to field offline conditions was proposed in this study. Based on MobileNets, a lightweight deep neural network with depthwise separable convolutions, and the transfer learning method, the proposed method was employed to establish a lithology recognition model for rock images. Its applications on mobile devices were verified, and the fast and accurate lithology identification of rock images was realized under the field offline conditions. Results demonstrate that the constructed model achieves 95.02% identification accuracy on the validation dataset and 93.45% identification accuracy on the test dataset of mobile devices. The average recognition time of each image is 1186 ms, and images which have result confidence higher than 95% respectively account for 91% of the test dataset. The mode size is 17.3 MB. These findings indicate that the model has high identification accuracy, short identification time, and reliable identification results. The proposed method provides good references for the establishment of a geological survey intelligent space and has promising prospects for application in field geological surveys. © 2018 Eastern Macedonia and Thrace Institute of Technology.
引用
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页码：111 / 117
页数：6
相关论文
共 24 条
  • [1] Li C., Lv X., Li F., Li J., Liu C., The architecture of geological survey intelligent space, Acta Geodaetica et Cartographica Sinica, 44, b12, pp. 143-151, (2015)
  • [2] Guo C., Liu Y., Research on Rock Image Recognition in Multi-Color Space, Science Technology and Engineering, 14, 18, pp. 247-251, (2014)
  • [3] Cheng G., Liu L., Feasibility study of deep learning algorithm applied to rock image processing, Software Guide, 15, 9, pp. 163-166, (2016)
  • [4] Cheng G., Ma W., Wei X., Rong C., Nan J., Petrofabric recognition based on image processing and neural network, Journal of Xi'an Shiyou University (Natural Science Edition), 28, 5, pp. 105-110, (2013)
  • [5] Lepisto L., Kunttu I., Visa A., Rock image classification using color features in Gabor space, Journal of Electronic Imaging, 14, 4, (2005)
  • [6] Singh V., Rao S.M., Application of image processing and radial basis neural network techniques for ore sorting and ore classification, Minerals Engineering, 18, 15, pp. 1412-1420, (2005)
  • [7] Singh N., Singh T.N Tiwary A., Textural identification of basaltic rock mass using image processing and neural network, Computational Geosciences, 14, 2, pp. 301-310, (2008)
  • [8] Cheng G., Yang J., Huang Q., Liu Y., Classification and recognition of rock thin section images based on probabilistic neural network, Science Technology and Engineering, 13, 31, pp. 9321-9325, (2013)
  • [9] Cheng G., Yin J., Rock slice image classification based on SVM, Technological Innovation and Application, 1, pp. 38-38, (2015)
  • [10] Zhang C., Hao L., Wang Y., Zhang Z., An image enhancement and lithology identification method based on Landsat8 OLI, Geology and Exploration, 53, 2, pp. 325-333, (2017)
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