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

    Froth image analysis by use of transfer learning and convolutional neural networks

    Access Status
    Fulltext not available
    Authors
    Fu, Y.
    Aldrich, Chris
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Fu, Y. and Aldrich, C. 2018. Froth image analysis by use of transfer learning and convolutional neural networks. Minerals Engineering. 115: pp. 68-78.
    Source Title
    Minerals Engineering
    DOI
    10.1016/j.mineng.2017.10.005
    ISSN
    0892-6875
    School
    WASM: Minerals, Energy and Chemical Engineering (WASM-MECE)
    URI
    http://hdl.handle.net/20.500.11937/68118
    Collection
    • Curtin Research Publications
    Abstract

    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 this paper, the use of a convolutional neural network, AlexNet, pretrained on a database of images of common objects was used as is to extract features from flotation froth images. These features could subsequently be used to predict the conditions or performance of the flotation systems. Two case studies are considered. In the first, froth regimes in an industrial flotation plant could be identified significantly more reliably with the features generated by AlexNet than with previous state-of-the-art approaches, such as wavelets, grey level co-occurrence matrices or local binary patterns. In the second case study, the arsenic concentration in the batch flotation of realgar-orpiment-quartz mixtures could be predicted more accurately than was possible with features extracted by wavelets, grey level co-occurrence matrices, local binary patterns or by use of colour. These results suggest that feature extraction with convolutional neural networks trained on complex data sets from other domains can serve as more reliable methods than previous state-of-the-art approaches to froth image analysis.

    Related items

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

    • Flotation Froth Image Analysis by Use of a Dynamic Feature Extraction Algorithm
      Fu, Y.; Aldrich, Chris (2016)
      Froth image analysis has been well established as a means to infer the performance of froth flotation cells in real time. Apart from linking the appearance of the froth to the behavior of the flotation system, the dynamic ...
    • Relationship between solids flux and froth features in batch flotation of sulphide ore
      Yang, X.; Aldrich, Chris (2005)
      The froth features in the batch flotation of a sulphide ore were investigated by using the digital image parameters of the froth, the small number emphasis (N sne ), the average grey level (D agl) and the instability ...
    • The estimation of platinum flotation grade from froth image features by using artificial neural networks
      Marais, C.; Aldrich, Chris (2011)
      The use of machine vision in the monitoring and control of metallurgical plants has become a very attractive option in the last decade, especially since computing power has increased drastically inthe last few years. The ...
    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.