文献类型：会议论文

英文题名：Weight Estimation Method of Coal and Gangue on Conveyor Belt Based on Instance Segmentation

作者：Li, Dongjun[1]; Meng, Guoying[1]; Sun, Zhiyuan[2]; Xu, Lili[3]; Cui, Wei[1]

第一作者：Li, Dongjun

机构：[1] School of Mechanical Electrical Information Engineering, China University of Mining and Technology, Beijing, Beijing, China; [2] College of Applied Science Technology, Beijing Union University, Beijing, Beijing, China; [3] Community Service Management, University of Science and Technology, Beijing, Beijing, China

第一机构：School of Mechanical Electrical Information Engineering, China University of Mining and Technology, Beijing, Beijing, China

会议论文集：Proceedings - 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture, AIAM 2021

会议日期：October 23, 2021 - October 25, 2021

会议地点：Manchester, United kingdom

语种：英文

外文关键词：Belt conveyors - Coal industry - Deep learning - Regression analysis

摘要：Due to the factor of energy structure, coal will be the main energy in China for a long time in the future. With mechanized mining, gangue will be mixed into the coal. In order to improve the efficiency of coal production and realize intelligent sensing, the weight statistics of coal and gangue on the conveyor belt have become a hot research topic for many researchers. At present, the weight statistics of coal and gangue are mainly carried out through washing analysis in the subsequent processing links, but the measurement results cannot be timely fed back and guide the optimization of mining work, which restricts the construction of intelligent mines. In this paper, an image-based weight estimation system of coal and gangue on conveyor belt is established by combining deep learning-based case segmentation algorithm and linear regression model. The experimental results show that the whole system has good stability and the measurement accuracy is 87%, which provides a new intelligent solution for coal production and utilization. ? 2021 IEEE.