Image-based thickener mud layer height prediction with attention mechanism-based CNN.

Mud layer height of thickener is the key quality index of thickening process which is difficult to achieve real-time detection with existing methods in reality. While the need of developing a soft sensor model which can be used for real-time detection of mud layer height, we proposed an end-to-end mud layer height prediction method with attention mechanism-based convolutional neural network (CNN). The dynamic features are firstly extracted from the image samples based on CNN, and then two types of attention mechanism are embedded sequentially to contribute to more precise prediction results. Compared with the traditional spatial attention mechanism, the regional spatial attention mechanism we proposed selectively divides the spatial feature map into regions, while regions containing important features are assigned larger weights. Adding the channel and regional spatial attention mechanism in CNN not only effectively improve both the precision and calculation speed, but also affect the dimension of the output feature map, so as to avoid the loss of channel or spatial attention information of the feature map. To verify the validity of the proposed method, different attention mechanisms are embedded in the CNN, and the corresponding experiments are carried out on the dataset of the thickener mud layer. The experimental results demonstrate the feasibility and effectiveness of the mud layer height prediction method. • A novel convolution neural network model combined with attention mechanism is designed for mud layer height of the thickener prediction. • A regional spatial attention mechanism is proposed which divides the feature map into regions in the spatial dimension according to the distribution of features, and the weights of each region are constructed to increase the weights of the regions containing the key information. • Applying the proposed method of DAM with region divided to the thickening process improves the network features extraction capability, and the prediction accuracy. [ABSTRACT FROM AUTHOR]