摘要
为实现摇床的实时监测和自动调整接矿板的位置,采用机器视觉技术采集摇床实时矿带照片,研究了实时矿带图像识别算法,开发了摇床实时图像监测智能设备。工业试验表明,摇床实时图像监测智能设备能够自动采集矿带图片,图像识别算法实现了对摇床矿带带宽、矿带边界和矿带颜色特征的数字化解析,实现根据矿带变化实时自动调节接矿板的位置。该设备提升了选矿的各项指标,降低了人工成本。
For purpose of realizing real-time monitoring of the shaker and automatically adjusting the position of ore connecting plates,the real-time ore belt photos of the shaker were collected by using machine vision technologies,and the real-time ore belt image recognition algorithm was studied,including the development of intelligent equipment for real-time image monitoring of the shaker.The industrial testing shows that,the shaker’s real-time image monitoring intelligent device can auto-collect ore bed images,and the image recognition algorithm can realize digital analysis of the shaking ore belt width,the ore belt and the ore belt color features,including auto-regulating position of the ore-connecting plates at real time according to the ore belt changes.This device can improve the indexes of mineral processing and reduce the labor cost.
作者
张文康
刘丹
杜钰
王春景
余龙舟
袁林逊
ZHANG Wen-kang;LIU Dan;DU Yu;WANG Chun-jing;YU Long-zhou;YUAN Lin-xun(Faculty of Land and Resources Engineering,Kunming University of Science and Technology;State Key Laboratory of Clean Utilization of Complex Nonferrous Metal Resources,Kunming University of Science and Technology;Yunnan Key Laboratory of Green Separationand Enrichment of Strategic Mineral Resources,Kunming University of Science and Technology;Yunnan Amde Electrical Engineering Co.,Ltd.;Yunnan Pinshi Intelligent Technology Co.,Ltd.)
出处
《化工自动化及仪表》
CAS
2023年第1期82-87,共6页
Control and Instruments in Chemical Industry
关键词
图像处理
摇床
智能
机器学习
image processing
shaking table
intelligent
machine learning
