SBSNet: selective branch segmentation network of coal flotation froth images

Abstract

ABSTRACT Flotation is a physical and chemical process that uses the difference between mineral surface properties to separate valuable minerals from worthless minerals through the attachment of bubbles. The visual information of the flotation foam surface can reflect the flotation effect and will be closely related to the flotation conditions, directly indicating the degree of mineralization of the foam layer. Considering the problem that coal slurry foam images are difficult to segment due to the mutual adhesion of bubbles and fuzzy boundaries, in this work, we proposed a new and effective segmentation algorithm for extracting coal slurry flotation foam edge information. Based on the encoder and decoder structure, a selective multi-branch input segmentation deep neural network model was designed. We evaluated the proposed method and model using a self-built coal slurry foam image segmentation dataset. The intersection over union, mean intersection over union, and PA evaluation indicators of this method increased by 12.8376%, 7.2716%, and 1.457%, respectively, compared with those of the U-Net model. The experimental results showed that the selective branch segmentation network model had high accuracy and robustness in extracting coal slurry bubble edge information, and it could effectively solve the problem of difficult segmentation of coal slurry foam images. This study provided a foundation for optimizing flotation process parameters and realizing intelligent flotation site parameters.