摘要
煤流量双目视觉测量是实现带式输送机节能安全运行控制的关键技术,但煤料纹理颜色重复单一和煤料颗粒内部间隙分布不均会严重影响煤流量测量精度和实时性。为此,提出基于深度学习的带式输送机煤流量双目视觉测量方法。首先,对煤料图像进行校正、分割和增强预处理;其次,构建基于深度学习的煤料立体匹配PSM-Net模型,运用Fine-tuning学习机制对PSM-Net进行模型训练,获取煤料体积;然后,依据煤料二维平面特征,提出基于离散元法的煤料堆积填充率计算方法,计算煤料堆积密度;最后,依据煤料体积和堆积密度计算带式输送机煤流量。实验结果验证了所提算法的有效性,煤流量双目视觉测量的精度达到98.7043%,计算速率达到1127 ms/帧。
The binocular vision measurement of coal flow is a key technology to realize energy-saving and safe operation control of belt conveyors.However,the texture and color features of coal samples are single and repeated.The coal particles′internal gaps are distributed uneven.These factors have seriously influence on the accuracy and real-time performance of coal flow measurement.To address these issues,a binocular vision measurement method for coal flow of belt conveyors is proposed,which is based on deep learning.Firstly,the coal image is preprocessed through correction,segmentation and enhancement.Secondly,a PSM-Net model for coal stereo matching is formulated,which is also based on deep learning.The fine-tuning learning mechanism is adopted to train the PSM-Net model to obtain the coal material volume.Then,based on the two-dimensional characteristics of coal material,a calculation method for coal packing rate based on discrete element method is proposed to achieve coal packing density.Finally,the coal flow of the belt conveyors is calculated,which is based on the obtained volume and packing density.Experiment results show the effectiveness of the proposed algorithm.The accuracy of the binocular vision measurement of coal flow reaches 98.7043%,and the calculation rate reaches 1127 ms per frame.
作者
杨春雨
顾振
张鑫
周林娜
Yang Chunyu;Gu Zhen;Zhang Xin;Zhou Linna(School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China;Engineering Research Center of Intelligent Control for Underground Space,Ministry of Education,China University of Mining and Technology,Xuzhou 221116,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2021年第8期164-174,共11页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(61873272,61741318)项目资助。
关键词
深度学习
离散元法
带式输送机
煤流量
双目视觉
deep learning
discrete element method
belt conveyor
coal flow
binocular vision
