[Poster Presentation]Load prediction of bucket elevator for jigging sorting based on binocular vision and Global Filter Networks
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[Poster Presentation]Load prediction of bucket elevator for jigging sorting based on binocular vision and Global Filter Networks

Load prediction of bucket elevator for jigging sorting based on binocular vision and Global Filter Networks
ID:329 Submission ID:200 View Protection:ATTENDEE Updated Time:2024-05-16 18:50:41 Hits:413 Poster Presentation

Start Time:Pending (Asia/Shanghai)

Duration:Pending

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Abstract
 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.
Keywords
Mass of coal and gangue,Binocular vision,Global Filter Networks,Stereo matching
Speaker
Jiawei Li
研究生 China University of Mining and Technology

Submission Author
佳伟 李 中国矿业大学
东阳 窦 中国矿业大学
国栋 黄 中国矿业大学
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