Recent Advances in Flotation Froth Image Analysis
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 colour of the froth and bubble size distributions, research has continued to focus...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
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Elsevier
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/97648 |
| _version_ | 1848766298132054016 |
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| author | Aldrich, Chris Avelar, Erica Liu, Xiu |
| author_facet | Aldrich, Chris Avelar, Erica Liu, Xiu |
| author_sort | Aldrich, Chris |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 colour of the froth and bubble size distributions, research has continued to focus on their use in automated control systems, which is not well established yet. This includes functionality related to the recognition of different operational regimes, as well as their use in the inferential measurement of froth grade. The last decade has seen major breakthroughs in deep learning and advances in image processing, which have also had a direct impact on flotation froth image analysis with computer vision systems. In this paper, these advances are reviewed and future trends are identified. Convolutional neural networks that are able to learn features from froth images have redefined the state-of-the-art in froth image analysis. These models rely heavily on transfer learning, with models such as GoogLeNet and MobileNet leading in the field. Emerging trends comprise a stronger focus on dynamic froth image analysis or the analysis of froth video sequences, froth-based monitoring, exploitation of froth features in advanced control and one-shot learning approaches based on froth image synthesis. Challenges are related to the labelling of images, the computational cost associated with training deep neural networks, as well as interpretation of these models. |
| first_indexed | 2025-11-14T11:48:55Z |
| format | Journal Article |
| id | curtin-20.500.11937-97648 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:48:55Z |
| publishDate | 2022 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-976482025-04-30T00:45:44Z Recent Advances in Flotation Froth Image Analysis Aldrich, Chris Avelar, Erica Liu, Xiu Science & Technology Technology Physical Sciences Engineering, Chemical Mineralogy Mining & Mineral Processing Engineering Flotation froth image analysis Computer vision Artificial intelligence Deep learning Convolutional neural networks Acknowledgements The authors acknowledge funding support from the Australian Research Council for the ARC Centre of Excellence for Enabling Eco-Efficient Beneficiation of Minerals grant number CE200100009 BUBBLE-SIZE DISTRIBUTION CONVOLUTIONAL NEURAL-NETWORKS MINERAL FLOTATION COOCCURRENCE MATRIX PREDICTIVE CONTROL SEGMENTATION ALGORITHM TEXTURE EXTRACTION SOFT SENSOR RECOGNITION PERFORMANCE 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 colour of the froth and bubble size distributions, research has continued to focus on their use in automated control systems, which is not well established yet. This includes functionality related to the recognition of different operational regimes, as well as their use in the inferential measurement of froth grade. The last decade has seen major breakthroughs in deep learning and advances in image processing, which have also had a direct impact on flotation froth image analysis with computer vision systems. In this paper, these advances are reviewed and future trends are identified. Convolutional neural networks that are able to learn features from froth images have redefined the state-of-the-art in froth image analysis. These models rely heavily on transfer learning, with models such as GoogLeNet and MobileNet leading in the field. Emerging trends comprise a stronger focus on dynamic froth image analysis or the analysis of froth video sequences, froth-based monitoring, exploitation of froth features in advanced control and one-shot learning approaches based on froth image synthesis. Challenges are related to the labelling of images, the computational cost associated with training deep neural networks, as well as interpretation of these models. 2022 Journal Article http://hdl.handle.net/20.500.11937/97648 10.1016/j.mineng.2022.107823 English Elsevier fulltext |
| spellingShingle | Science & Technology Technology Physical Sciences Engineering, Chemical Mineralogy Mining & Mineral Processing Engineering Flotation froth image analysis Computer vision Artificial intelligence Deep learning Convolutional neural networks Acknowledgements The authors acknowledge funding support from the Australian Research Council for the ARC Centre of Excellence for Enabling Eco-Efficient Beneficiation of Minerals grant number CE200100009 BUBBLE-SIZE DISTRIBUTION CONVOLUTIONAL NEURAL-NETWORKS MINERAL FLOTATION COOCCURRENCE MATRIX PREDICTIVE CONTROL SEGMENTATION ALGORITHM TEXTURE EXTRACTION SOFT SENSOR RECOGNITION PERFORMANCE Aldrich, Chris Avelar, Erica Liu, Xiu Recent Advances in Flotation Froth Image Analysis |
| title | Recent Advances in Flotation Froth Image Analysis |
| title_full | Recent Advances in Flotation Froth Image Analysis |
| title_fullStr | Recent Advances in Flotation Froth Image Analysis |
| title_full_unstemmed | Recent Advances in Flotation Froth Image Analysis |
| title_short | Recent Advances in Flotation Froth Image Analysis |
| title_sort | recent advances in flotation froth image analysis |
| topic | Science & Technology Technology Physical Sciences Engineering, Chemical Mineralogy Mining & Mineral Processing Engineering Flotation froth image analysis Computer vision Artificial intelligence Deep learning Convolutional neural networks Acknowledgements The authors acknowledge funding support from the Australian Research Council for the ARC Centre of Excellence for Enabling Eco-Efficient Beneficiation of Minerals grant number CE200100009 BUBBLE-SIZE DISTRIBUTION CONVOLUTIONAL NEURAL-NETWORKS MINERAL FLOTATION COOCCURRENCE MATRIX PREDICTIVE CONTROL SEGMENTATION ALGORITHM TEXTURE EXTRACTION SOFT SENSOR RECOGNITION PERFORMANCE |
| url | http://hdl.handle.net/20.500.11937/97648 |