Intelligent Deployment Solution for Tabling Adapting Deep Learning

The deep learning object detection model can well extract the coordinate information of the separating point of the concentrate ore belt boundary line. However, with the drawbacks of acquired zonal image features, it is difficult to obtain the perfect prediction of the concentrate grade and recovery rate. In this study, by adapting deep learning semantic segmentation technique with DeepLab V3+, which can effectively extract multi-dimensional image features. The mapping relationship between image features and attributes of the Tabling equipment is achieved by constructing a multi-output support vector regression model optimized of a sparrow search algorithm (SSA-MSVR). The beneficiation indicators and operating parameters of the Tabling can be continually detected and optimized by an intelligent system that mainly includes image recognition softwares, automatic control units, data processing and communication workstations, and matching intelligent equipment, which accomplished the intelligent deployment solution of the Tabling beneficiation process.