Abstract
The commonly used working condition recognition method in the mineral flotation process is based on shallow features of flotation froth images. However, the shallow features of flotation froth images frequently have an excessive amount of redundant and noisy information, which has an impact on the recognition effect and prevents the flotation process from being effectively optimized. Therefore, a working condition recognition method for the mineral flotation process based on a deep and shallow feature fusion densely connected network decision tree (DSFF-DenseNet-DT) is proposed in this paper. Firstly, the color texture distribution (CTD) and size distribution (SD) of a flotation froth image obtained in advance are approximated by the nonparametric kernel density estimation method, and a set of kernel function weights is obtained to represent the color texture and size features, while the deep features of the flotation froth image are extracted through the densely connected network (DenseNet). Secondly, a two-stage feature fusion method based on a stacked autoencoder after Concat (Cat-SAE) is proposed to fuse and reduce the dimensionality of the extracted shallow features and deep features so as to maximize the comprehensive description of the features and eliminate redundant and noisy information. Finally, the feature vectors after fusion dimensionality reduction are fed into the densely connected network decision tree (DenseNet-DT) for working condition recognition. Multiple experiments employing self-built industrial datasets reveal that the suggested method's average recognition accuracy, precision, recall and F1 score reach 92.67%, 93.9%, 94.2% and 0.94, respectively. These results demonstrate the proposed method's usefulness.