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.

机器视觉广泛用于泡沫浮选设备的监控，作为辅助设备控制操作员的一种手段。虽然这些系统具有分析物理泡沫特征的成熟能力，例如泡沫颜色和气泡尺寸分布，但研究继续集中在它们在自动化控制系统中的使用，这还没有很好地建立。这包括与识别不同操作制度相关的功能，以及它们在泡沫等级推断测量中的用途。在过去的十年中，深度学习和图像处理取得了重大突破，这也对使用计算机视觉系统进行浮选泡沫图像分析产生了直接影响。本文回顾了这些进展并确定了未来趋势。能够从泡沫图像中学习特征的卷积神经网络重新定义了泡沫图像分析的最新技术。这些模型严重依赖迁移学习，GoogLeNet 和 MobileNet 等模型在该领域处于领先地位。新兴趋势包括更加关注动态泡沫图像分析或泡沫视频序列分析、基于泡沫的监控、在高级控制中利用泡沫特征以及基于泡沫图像合成的一次性学习方法。挑战与图像的标记、与训练深度神经网络相关的计算成本以及这些模型的解释有关。GoogLeNet 和 MobileNet 等模型在该领域处于领先地位。新兴趋势包括更加关注动态泡沫图像分析或泡沫视频序列分析、基于泡沫的监控、在高级控制中利用泡沫特征以及基于泡沫图像合成的一次性学习方法。挑战与图像的标记、与训练深度神经网络相关的计算成本以及这些模型的解释有关。GoogLeNet 和 MobileNet 等模型在该领域处于领先地位。新兴趋势包括更加关注动态泡沫图像分析或泡沫视频序列分析、基于泡沫的监控、在高级控制中利用泡沫特征以及基于泡沫图像合成的一次性学习方法。挑战与图像的标记、与训练深度神经网络相关的计算成本以及这些模型的解释有关。