基于深度学习的矿石图像处理研究综述  

A survey of ore image processing based on deep learning

作　　者：王伟 李擎[1,2] 张德政[3,4] 栗辉 王昊[1] WANG Wei;LI Qing;ZHANG De-zheng;LI Hui;WANG Hao(School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China;Key Laboratory of Knowledge Automation for Industrial Processes(Ministry of Education),Beijing 100083,China;School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China;Beijing Key Laboratory of Knowledge Engineering for Materials Science,Beijing 100083,China)

机构地区：[1]北京科技大学自动化学院,北京100083 [2]工业过程知识自动化教育部重点实验室,北京100083 [3]北京科技大学计算机与通信工程学院,北京100083 [4]材料领域知识工程北京市重点实验室,北京100083

出　　处：《工程科学学报》2023年第4期621-631,共11页Chinese Journal of Engineering

基　　金：国家自然科学基金资助项目(62173029);北京科技大学中央高校基本科研业务费资助项目(FRF-IC-20-03);河北省高等学校科学技术研究项目(QN2019184)。

摘　　要：聚焦于矿石勘探和将矿石破碎筛分后的皮带运输两个环节,系统总结了深度学习技术在矿石图像处理中的主要应用,包括矿石分类、粒度分析和异物识别等任务,并分门别类地梳理了完成以上三大任务的常用算法及其优缺点.其中,矿石分类在地质勘探中起着重要作用;粒度分析能为破碎机和传送皮带的控制提供参考依据,还能识别出给矿皮带上过大尺寸的矿石,防止处于给矿皮带和受矿皮带之间的转运缓冲仓内发生堵料事故;异物识别能将皮带上混在矿石中的有害物品检测出来.Ore is an essential industrial raw material and strategic resource that plays an important role in China’s economic construction.The smart mine aims to build an unmanned,efficient,intelligent,and remote factory to improve quality,reduce cost,save energy,and increase the efficiency of mineral resource extraction.Ore image processing technology can automatically and efficiently complete a series of difficult and repetitive tasks,which constitutes an important part of smart mine construction.However,open-air operation modes,high-dust environments,and ore diversity have brought great challenges to ore image processing.Benefiting from its strong automatic feature extraction ability,deep learning can deeply perceive a complex environment,which enables it to play an important role in the ore image processing field and help traditional mining companies transform into efficient,green,and intelligent enterprises.This paper focuses on two production stages,including ore prospecting and belt transportation.We systematically summarize the main applications of deep learning in ore image processing,including ore classification,particle size analysis,and foreign material recognition,sort out the corresponding algorithms,and analyze their advantages and disadvantages.Specifically,according to the number of ores in an image,ore classification is divided into single-object and multi-object classifications.Single-object classification is mostly addressed by image classification networks,while multi-object classification is mostly accomplished by object detection and semantic segmentation networks.Single-object classification plays an important role in geological prospecting.Particle size refers to the size information of ores in an image.Generally,it can be divided into three modes:particle size statistics,particle size classification,and large block detection.Among these modes,the first and the third are mainly used in actual industrial production.Particle size statistics are determined mostly using semantic segmentation networks

关 键 词：深度学习 矿石图像处理 矿石分类 粒度分析 异物识别 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]