基于状态识别的高炉料面视频关键帧提取方法  

Key Frame Extraction Method of Blast Furnace Burden Surface Video Based on State Recognition

作　　者：黄建才 蒋朝辉[1,2] 桂卫华 潘冬[1] 许川[1] 周科 HUANG Jian-Cai;JIANG Zhao-Hui;GUI Wei-Hua;PAN Dong;XU Chuan;ZHOU Ke(School of Automation,Central South University,Changsha 410000;Peng Cheng Laboratory,Shenzhen 518000)

机构地区：[1]中南大学自动化学院,长沙410000 [2]鹏城实验室,深圳518000

出　　处：《自动化学报》2023年第11期2257-2271,共15页Acta Automatica Sinica

基　　金：国家重大科研仪器研制项目(61927803);国家自然科学基金基础科学中心项目(61988101);湖南省科技创新计划(2021RC4054);湖南省研究生科研创新项目(CX20210243);中南大学中央高校基本科研业务费专项资金(2021zzts0184)资助。

摘　　要：高炉料面视频关键帧是视频中的中心气流稳定、清晰、无炉料及粉尘遮挡且特征明显的图像序列,对于及时获取炉内运行状态、指导炉顶布料操作具有重要的意义.然而,由于高炉内部恶劣的冶炼环境及布料的周期性和间歇性等特征,料面视频存在信息冗余、图像质量参差不齐、状态多变等问题,无法直接用于分析处理.为了从大量高炉冶炼过程料面视频中自动准确筛选清晰稳定的料面图像,提出基于状态识别的高炉料面视频关键帧提取方法.首先,基于高温工业内窥镜采集高炉冶炼过程中的料面视频,并清晰完整给出料面反应新现象和形貌变化情况;然后,提取能够表征料面运动状态的显著性区域的特征点密集程度和像素位移特征,并提出基于局部密度极大值高斯混合模型(Local density maxima-based Gaussian mixture model,LDGMM)聚类的方法识别料面状态;最后,基于料面状态识别结果提取每个布料周期不同状态下的关键帧.实验结果表明,该方法能够准确识别料面状态并剔除料面视频冗余信息,能提取出不同状态下的料面视频关键帧,为优化炉顶布料操作提供指导.The key frames of the blast furnace burden surface video are the clear image sequences with stable central airflow,no burden and dust occlusion,and obvious characteristics,which are of great significance for timely obtaining the running state of the blast furnace and guiding the charging operation.However,due to the harsh ironmaking environment inside the blast furnace,the periodic and intermittent characteristics of the burden distribution,the burden surface video has problems such as redundant information,uneven image quality and changeable state,which cannot be directly used for analysis and processing.To screen clear and stable burden surface images automatically and accurately from a large number of burden surface videos during the blast furnace ironmaking process,a key frame extraction method of blast furnace burden surface video based on state recognition is proposed.Firstly,the burden surface video in the blast furnace ironmaking process is collected based on the high-temperature industrial endoscope,and the new phenomenon and change of burden surface topography are given clearly and completely.Then,the feature point density and pixel displacement characteristics in the salient region that can characterize the burden surface motion state are extracted.Next,a method of local density maxima-based Gaussian mixture model(LDGMM)clustering is proposed to recognize the burden surface state.Finally,the key frames in different states of each burden distribution cycle are extracted based on the state recognition results of the burden surface.The experimental results show that this method can accurately recognize the burden surface state,eliminate the redundant information of the burden surface video,and extract the key frames of the burden surface video under different states,which provides guidance for optimizing the furnace top charging operation.

关 键 词：高炉 料面现象 显著性区域 状态识别 关键帧提取 

分 类 号：TF54[冶金工程—钢铁冶金] TP391.41[自动化与计算机技术—计算机应用技术]