Recent Advances in Flotation Froth Image Analysis

Abstract

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.