In jigging sorting, products are elevated by bucket elevators and transported via a shared belt. The product weight of each jig cannot be detected. The output of each product is the key parameter to calculate the jigging separation efficiency and measure the separation effect. At the same time, the bucket elevator overload will cause accidents, resulting in deformation and rupture of the bucket. To solve this problem, an intelligent monitoring scheme of bucket elevators based on binocular vision and deep learning was proposed. The YOLACT algorithm was utilized for coal and gangue image segmentation. Aiming at the problem of multi-layer accumulation of materials in the bucket elevator, a Global Filter Networks (GFNet) based on the Pyramid Stereo Matching Network was used for more accurate stereo matching, and statistical filtering was used to denoise the generated 3D point cloud. The volume prediction model was established by integral. The Bootstrap method was used to obtain the best empirical density of coal and gangue. Finally, the volume parameters and density parameters were multiplied to get the mass of coal and gangue. The experimental results showed that the GFNet algorithm can obtain clear 3D point clouds. The average error of the volume calculation model based on these 3D point clouds was 4.19%. In the single-machine test of the coal preparation plant, the proposed system realized the real-time detection of the mass of materials carried by the bucket elevator. Compared with the electronic belt scale, the average error was 10.55 %, which meets the industrial demand.

Mass of coal and gangue,Binocular vision,Global Filter Networks,Stereo matching