Skip to main content
SpringerLink
Log in
Book cover

The International Conference on Cyber Security Intelligence and Analytics

CSIA 2021: Cyber Security Intelligence and Analytics pp 405–410Cite as

Experimental Study on Intelligent Mineral Recognition Under Microscope Based on Deep Learning

  • Conference paper
  • First Online:

  • 1089 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1342))

Abstract

Intelligent ore mineral identification is one of the basic technologies of intelligent geology and intelligent ore deposit science. Computer vision technology and deep learning theory make the intelligent identification of ore and mineral possible. Based on tensorflow, a deep learning system, this study takes pyrite, chalcopyrite, galena, sphalerite and other sulfide minerals in Jiapigou gold mine and Shihu gold mine in Hebei Province as examples,The experimental results show that the accuracy of the model increases and the loss function decreases with the increase of training times; after 3000 batch processing, the model accuracy and loss function tend to be stable. The recognition success rate of the training model for the microscopic ore and mineral photos in the test set is higher than 90%.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bianco, S., Buzzelli, M., Mazzini, D., Schettini, R.: Deep learning for logo recognition. Neurocomputing 245, 23–30 (2017)

    Article  Google Scholar 

  2. Do, K.D.: Formation tracking control of unicycle-type mobile robots with limited sensing ranges. Rob. Auton. Syst. 16(3), 527–538 (2008)

    Google Scholar 

  3. Guo, X.J.: Image quasi dense matching and cosegmentation. Ph.D. Dissertation. Tianjin University, Tianjin (2013) ( in Chinese with English summary)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. China University of Geosciences, 29 Xueyuan Road, Haidian District, Beijing, 100083, China

    Lingfei Han

Authors
  1. Lingfei Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lingfei Han .

Editor information

Editors and Affiliations

  1. Shanghai University of Medicine and Health Sciences, Shanghai, China

    Zheng Xu

  2. Department Software Engineering and Game Development, Kennesaw State University, Marietta, GA, USA

    Reza M. Parizi

  3. School of Engineering and Sciences, Tecnologico de Monterrey, Puebla, Mexico

    Octavio Loyola-González

  4. Assistant Professor in Practice, University of Texas at San Antonio, Boerne, TX, USA

    Xiaolu Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Han, L. (2021). Experimental Study on Intelligent Mineral Recognition Under Microscope Based on Deep Learning. In: Xu, Z., Parizi, R.M., Loyola-González, O., Zhang, X. (eds) Cyber Security Intelligence and Analytics. CSIA 2021. Advances in Intelligent Systems and Computing, vol 1342. Springer, Cham. https://doi.org/10.1007/978-3-030-70042-3_58

Download citation

Publish with us

Policies and ethics

Search

Navigation