Abstract:During flotation, it is crucial for the flotation foam to maintain a certain degree of stability to ensure the stability of flotation indexes. Due to the complexity of the flotation froth itself and the limitations of the existing detection methods, it is currently not possible to quantitatively detect and evaluate the stability of the froth at industrial sites. For this reason, this paper develops a flotation froth image feature matching algorithm based on the improved SuperGlue model for the measurement of flotation froth stability. The algorithm uses the improved SuperPoint model network to extract feature points from the foam image, the original VGG network is improved, and the feature matching model is utilized to match the two sets of feature points obtained, and then mismatch refinement screening is carried out, and the confidence threshold is set to further improve the matching accuracy. Compared with the GMS algorithm, which is more widely used in matching algorithms, the effective feature point matching pairs of this paper"s algorithm are improved by 19.58%, and the matching accuracy reaches 99.85%. Compared with the traditional grayscale difference method, the foam stability measurement method in this paper improves the recognizability of foam in different states significantly, greatly improves the image detection sensitivity, and can meet the requirements of foam stability measurement in production.