Abstract: To counter existing limitations in paste concentration monitoring of paste backfilling technique, such as lack of accuracy, short life expectancy of related device, prolonged detection time, and limitations due to safety issues, a two-stream visual feature fusion model for automatic soft measuring of paste concentration is proposed, reducing the need for manual participation, increasing automation, and furthering the application of holistic artificial intelligence in the field of smart mining. The model, based on convolutional neural network model, adopts a two-stream architecture, analyzes the paste video and the corresponding optic flow information, extracts spatial and temporal features from the input, and produces two-stream feature representation. The feature fusion module further enhances representation for effective features, enabling the model to accurately measure paste concentration through non-contact method. In addition, video data of paste under production environment is collected to construct a dataset, and evaluated the proposed model with the dataset. The experiment results show that the proposed model can reach an accuracy of 94.16% , surpassing other deep learning methods by 3.47% under the same condition, fulfilling the need to conduct accurate real-time paste concentration detection in production environment.

Key words: paste backfilling , precise paste concentration prediction ,  video classification ,  multi-modal