实时图像驱动的电熔镁炉虚拟可视化监控系统
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摘要
电熔镁炉生产过程复杂,目前仍需要操作员在现场巡检,环境恶劣,危险性高。针对视频监控受到高温、粉尘、强光的影响,无法获得高质量的监控画面等问题,提出一种实时图像驱动的电熔镁炉虚拟可视化监控系统,在低质量的视频监控画面的基础上,利用图像去雾技术、数字抠图技术、颜色迁移技术等实时重建虚拟的电熔镁炉以及炉口火焰,生成的虚拟电熔镁炉可视化监控可远程发布到不同平台的终端,用于远程/移动的工况监控。该系统在工业生产中进行了实验,结果表明虚拟监控系统能够实时反映真实生产中镁炉炉口火焰的动态特征,为操作员的生产监控提供了新的工具。
Fused magnesium furnace production process is complex, which still needs frequently inspection by the operators. Meanwhile, the environment is harsh and accompanied with high risk. Video surveillance cannot obtain high quality images due to high temperature, dust and light effects. This paper presents a real-time image-driven virtual visualization monitoring system for fused magnesia furnace. Based on the low quality video surveillance images, the virtual fused magnesium of furnace and furnace flame are reconstructed in real time by using several technologies including image dehazing, digital matting and color migration. The visual monitoring of virtual fused magnesium furnace can be remotely distributed to different platforms for remote/mobile conditions monitoring. The system has been experimented in industrial production. The results show that the virtual monitoring system can reflect the dynamic characteristics of the furnace flame in real time and provide a new tool for the operator's production monitoring.
Fused magnesium furnace production process is complex, which still needs frequently inspection by the operators. Meanwhile, the environment is harsh and accompanied with high risk. Video surveillance cannot obtain high quality images due to high temperature, dust and light effects. This paper presents a real-time image-driven virtual visualization monitoring system for fused magnesia furnace. Based on the low quality video surveillance images, the virtual fused magnesium of furnace and furnace flame are reconstructed in real time by using several technologies including image dehazing, digital matting and color migration. The visual monitoring of virtual fused magnesium furnace can be remotely distributed to different platforms for remote/mobile conditions monitoring. The system has been experimented in industrial production. The results show that the virtual monitoring system can reflect the dynamic characteristics of the furnace flame in real time and provide a new tool for the operator's production monitoring.
引文
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[3]王毅,李宏光.智能体在工业监控系统中的应用[J].自动化仪表,2014,35(2):61-65.Wang Y,Li H G.Application of Intelligent Agent in Industrial Process Monitoring System[J].Process Automation Instrumentation,2014,35(2):61-65.
[4]Zhang G."Real time visualization of large terrain in flight simulation,"2008 Asia Simulation Conference[C].Beijing,2008,36-39.
[5]吴志伟,方正,柴天佑,等.电熔镁炉嵌入式专用控制器及其控制方法研究[J].仪器仪表学报,2012,6(33):1261-1267.Wu Z W,Fang Z,Chai T Y,et al.Research on Special Embedded Controller and Its Control Method for Fused Magnesium Furnace[J].Chinese Journal of Scientific Instrument,2012,33(6):1261-1267.
[6]吴翠先,李丹阳,马忍,等.基于快速引导滤波改进的暗原色先验去雾算法[J].电视技术,2016,40(6):22-27.Wu C X,Li D Y,Ma R,et al.Improved algorithm of haze removal base on dark channel prior and fast guided filter[J].Video Engineering,2016,40(6):22-27.
[7]张雅琼.基于K-Means的无线传感网均匀分簇路由算法研究[J].控制工程,2015,22(6):1181-1185.Zhang Y Q.Research on Uniform Clustering Routing Algorithm Based on K-means in Wireless Sensor Network[J].Control Engineering of China,2015,22(6):1181-1185.
[8]Mccartney E J,Jr F F H.Optics of the Atmosphere:Scattering by Molecules and Particles[J].Optica Acta International Journal of Optics,1977,24(7):779-779.
[9]He K M,Sun J,Tang X O.Guided image filtering[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2013,35(6):1397-1409.
[10]Freeman W,Adelson E.The design and use of steerable filters.IEEETrans.on Image Processing,1991,13(9):891-906.
[11]向光荣,张旗,徐发淙.数字图像处理在顶点识别中的应用研究[J].控制工程,2003,10(1):94-96.Xiang G R,Zhang Q,Xu F C.Study of Vertex Recognition of Digital Image Processing[J].Control Engineering of China,2003,10(1):94-96.
[12]Porter T,Duff T.Compositing Digital Images[M].New York:ACM,1998.
[13]Lee P,Wu Y.Nonlocal Matting[A].Computer Vision and Pattern Recognition[C].New York:IEEE,2011:2193-2200.
[14]Chen Q F,Li D Z Y,Tang CK.KNN Matting[A].Computer Vision and Pattern Recognition[C].IEEE,2012:869-876.
[15]Vedaldi A,Fulkerson B.Vlfeat:an Open and Portable Library of Computer Vision Algorithms[C].Firenze:DBLP,2010:1469-1472.
[16]Erik R,Michael A,Bruce G,et al.Color Transfer between Images[J].IEEE Computer Graphics&Applications,2001,21(5):34-41.