Measurement of bubble size and froth velocity using convolutional neural networks

Abstract

Bubble size and froth velocity are the most important froth characteristics used for evaluating and controlling flotation systems. Bubble size is often measured using watershed and edge detection algorithms. These algorithms typically result in the over-segmentation of big bubbles or the under-segmentation of small ones. Froth velocity is usually determined by processing successive images using various techniques such as pixel tracing, block matching, and bubble tracking. Most of these algorithms perform well but are quite slow. Pre-trained convolutional neural networks are more accurate and simpler models for image classification and feature extraction. In this research, a new approach based on using a pre-trained convolutional neural network to measure bubble size and froth velocity from froth flotation images is proposed. The results demonstrate that pre-trained convolutional neural networks can serve as a more reliable and faster approach than classical image processing algorithms for analyzing froth images.