Recent advances in flotation froth image analysis via deep learning

Abstract

Flotation froth image analysis with computer vision systems has witnessed a transformative evolution through the integration of deep learning. Deep learning outperforms traditional feature design by effectively learning intricate feature representations, thus enhancing the assessment of froth flotation processes' operational performance. Flotation froth image analysis via deep learning facilitates real-time monitoring of dynamic flotation processes, guiding the adjustment of operational variables through predicting performance indicators, recognizing froth states and segmenting foam edges, which promotes resource efficiency and supports the sustainable development of beneficiation. Despite the vast potential of deep learning for time-series forecasting within the multistage flotation cycle, its capabilities remain underexplored. To fill this gap, based on recent research, we discuss the application of temporal and multistage information in flotation cycle. We introduce the development trends of deep learning in various processes of flotation froth image analysis, including data collection, dataset preprocessing, feature extraction, and modeling. We particularly discuss advanced techniques for extracting time-series features, and developing multistage models and innovative data collection methods, so as to emphasize the importance of using temporal information. Eventually, the review explores several trends and challenges for future research. This review is expected to leave readers with deeper thoughts about algorithm design and data collection in the flotation domain, thereby promoting further research and development in beneficiation automation.