Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Recognition of flotation froth conditions with k-shot learning and convolutional neural networks

    Recognition of Flotation Froth Conditions with k-Shot Learning and Convolutional Neural Networks.pdf (862.7Kb)
    Access Status
    Open access
    Authors
    Liu, Xiu
    Aldrich, Chris
    Date
    2023
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Liu, X. and Aldrich, C. 2023. Recognition of flotation froth conditions with k-shot learning and convolutional neural networks. Journal of Process Control. 128: pp. 103004-103004.
    Source Title
    Journal of Process Control
    DOI
    10.1016/j.jprocont.2023.103004
    ISSN
    0959-1524
    Faculty
    Faculty of Science and Engineering
    School
    WASM: Minerals, Energy and Chemical Engineering
    URI
    http://hdl.handle.net/20.500.11937/97647
    Collection
    • Curtin Research Publications
    Abstract

    In this study, previous work on k-shot learning in flotation froth image analysis using small sets of froth images, is extended. As before, image synthesis is used to augment these data sets, but in addition, fine-tuning of convolutional neural networks, as well as smaller data sets with as few as 10 samples per class are considered. Two convolutional neural network models, namely AlexNet and GoogLeNet, were compared and the latter demonstrated better performance generally. Both performed better than a traditional approach based on the use of image features derived from local binary patterns. Fine-tuning of the convolutional neural networks markedly improved their performance compared to pure transfer learning without fine-tuning. Moreover, a case study with platinum froth images showed that as few as only 10–30 real images per class are needed to achieve reasonably good classification performance, i.e. the ability to recognize four different classes of froths with an accuracy of 63%–84%, if AlexNet or GoogLeNet with fine-tuning is used. Using 100 real images per class with a fine-tuned GoogLeNet resulted in predictive accuracy approaching 92%. These results are comparable to those achievable with traditional approaches to feature extraction based on training data sets up to two orders of magnitude larger.

    Related items

    Showing items related by title, author, creator and subject.

    • Recent Advances in Flotation Froth Image Analysis
      Aldrich, Chris ; Avelar, Erica; Liu, Xiu (2022)
      Machine vision is widely used in the monitoring of froth flotation plants as a means to assist control operators on the plant. While these systems have a mature ability to analyse physical froth features, such as the ...
    • Multivariate Image Processing in Minerals Engineering with Vision Transformers
      Liu, Xiu ; Aldrich, Chris (2024)
      Vision transformers (ViTs) are a new class of deep learning algorithms that have recently emerged as a competitive alternative to convolutional neural networks. In this investigation, their application to two operations ...
    • Froth image analysis by use of transfer learning and convolutional neural networks
      Fu, Y.; Aldrich, Chris (2018)
      Deep learning constitutes a significant recent advance in machine learning and has been particularly successful in applications related to image processing, where it can already surpass human accuracy in some cases. In ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.