焦炭光学组织图像的特征提取及分类识别  

Characteristics extraction and classification recognition of coke optical texture images

作　　者：李文超 闫立强 王保荣 王杰平 李光跃 Li Wenchao;Yan Liqiang;Wang Baorong;Wang Jieping;Li Guangyue(College of Chemical Engineering,North China University of Science&Technology,Tangshan 063210,China;Tangshan Shougang Jingtang Xishan Coking Co.,Ltd.,Tangshan 063200,China)

机构地区：[1]华北理工大学化学工程学院,唐山063210 [2]唐山首钢京唐西山焦化有限责任公司,唐山063200

出　　处：《燃料与化工》2021年第2期17-21,共5页Fuel & Chemical Processes

摘　　要：焦炭光学组织结构直接决定焦炭的质量。目前,焦炭光学组织含量测定主要是利用光学显微镜进行采集,操作过程较复杂,人工识别可能带来误差。基于图像分析方法,对焦炭光学组织图像中的各个显微组织结构进行特征截取。利用RGB像素值、颜色矩、LBP算法进行图像特征提取,并分别采用K-近邻、支持向量机和随机森林3种数学模型进行分类识别。精确度对比结果表明,在颜色矩提取的图像下,利用支持向量机模型计算方法识别焦炭光学组织的精度可达90%。The optical texture of coke directly determines the quality of coke.At present,the determination of optical texture content of coke is mainly collected by optical microscope,the operation process is complicated,and the manual identification may bring errors.Based on the image analysis,each microstructure in the optical image of the coke is subjected to characteristics interception.RGB pixel value,Color Moments and Local Binary Pattern(LBP algorithm)are used for extracting their images characteristics.K-Nearest Neighbor,Support Vector Machine and Random Forest are used for classification and identification respectively.The accuracy results show that the image characteristics extraction method based on Color Moment,using the Support Vector Machine model calculation method to identify the coke optical texture can reach 90%.

关 键 词：焦炭光学组织 特征提取 支持向量机 

分 类 号：TQ520.1[化学工程—煤化学工程]