Abstract:The surface characteristics of the froth in the mineral flotation process is an important indicator of flotation performance, which can reflect the change of flotation effect in real time and intuitively, and accurate segmentation of the edge information of the froth is an important task in the flotation process. In recent years, researchers have proposed various flotation froth image segmentation algorithms, but there are problems such as large number of froths, serious adhesion between froths and unclear edges in the flotation froth image, and the existing methods are unable to accurately segment froth edges due to the limited feature extraction capability. Accordingly, this paper proposes a multi-scale fusion-based edge segmentation algorithm for flotation froth images using deep learning, which enhances the feature extraction capability of the model by introducing a deep high-resolution coding structure and an attention-based hierarchical fusion method to improve the segmentation effect of flotation froth edges. Specifically, the deep high-resolution coding structure can simultaneously maintain feature information at different resolution levels, so that our network model can effectively capture information at different scales, improve image semantic comprehension while maintaining more detailed information, and improve the ability to process high-resolution and task-intensive images. In addition to this, this paper design an attention-based hierarchical fusion method to fully fuse the deep and shallow feature maps, so that the feature maps obtained from the fusion converge to more important feature information, thus improving the ability to recognize the boundaries of flotation froths and accurately locate flotation froths. The algorithm achieves the best performance on the froth boundary segmentation dataset with a froth IoU of 58.25 and a froth Fscore of 73.62, which proves that our proposed algorithm can segment flotation froth edges more accurately.