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    Using Convolutional Neural Networks to Develop State-of-the-Art Flotation Froth Image Sensors

    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. Using Convolutional Neural Networks to Develop State-of-the-Art Flotation Froth Image Sensors. IFAC-PapersOnLine. 51 (21): pp. 152-157.
    Source Title
    IFAC-PapersOnLine
    DOI
    10.1016/j.ifacol.2018.09.408
    ISSN
    2405-8963
    School
    WASM: Minerals, Energy and Chemical Engineering (WASM-MECE)
    URI
    http://hdl.handle.net/20.500.11937/73068
    Collection
    • Curtin Research Publications
    Abstract

    Convolutional neural networks provide a state-of-the-art approach to the development of froth image sensors. In this study, it is shown that a pretrained neural network architecture, namely VGG16, can be used to obtain significant improvements in froth image sensors. However, training of these networks is computationally demanding and require large data sets that may not be readily available. These problems can be circumvented by making use of transfer learning and partial retraining of the network. Likewise, minor modification of the network architecture can also expedite the development of the models. This is demonstrated in a case study involving an image data set from an industrial platinum group metals plant.

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