基于改进MSER算法的电力设备红外故障区域提取方法
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摘要
红外图像处理中因目标边界模糊、区域灰度变化等因素,导致传统的极大稳态区域方法区域提取效果低下。为此,提出一种基于改进极大稳态区域方法的电力设备红外故障区域提取机制,提升区域提取效果。首先,从灰度相似度聚类出发,采用Meanshift算法对分割区域的邻域像素进行聚类。其次,结合阈值分割机制,快速将相似像素进行分割,最终通过迭代得到电力设备故障所呈现的亮度区域信息。实验结果表明该提取区域方法性能优于极大稳态区域算法,具有较低的误分类错误,且相比于Mean shift算法,具有高效的处理速度。
Aiming at the problem of blur boundary and the intensity variation of regions in the infrared image, the traditional Maximally Stable Extremal Region(MSER) may fail to detect the region, thus leading to the poor performance.Therefore, the improved MSER algorithm is proposed in this paper to find the fault region in infrared electrical equipment image, which is based on the intensity similarity clustering. At first, the mean shift algorithm is used to cluster the pixels with similarity from the point of viewing of intensity homogeneity. Second, the thresholding mechanism is utilized to get the fast binary image, where the thresholding is selected from the low intensity of pixels clustered into the region. It thereby can split the pixels with similarity together faster, and the bright region corresponding to the fault region can be obtained through the iteration. Finally, experiments on the electrical equipment with infrared image show that the proposed method has better performance than the original MSER and owns lower misclassification error. Meanwhile, it decreases the time consumption as comparing to the Mean shift algorithm.
Aiming at the problem of blur boundary and the intensity variation of regions in the infrared image, the traditional Maximally Stable Extremal Region(MSER) may fail to detect the region, thus leading to the poor performance.Therefore, the improved MSER algorithm is proposed in this paper to find the fault region in infrared electrical equipment image, which is based on the intensity similarity clustering. At first, the mean shift algorithm is used to cluster the pixels with similarity from the point of viewing of intensity homogeneity. Second, the thresholding mechanism is utilized to get the fast binary image, where the thresholding is selected from the low intensity of pixels clustered into the region. It thereby can split the pixels with similarity together faster, and the bright region corresponding to the fault region can be obtained through the iteration. Finally, experiments on the electrical equipment with infrared image show that the proposed method has better performance than the original MSER and owns lower misclassification error. Meanwhile, it decreases the time consumption as comparing to the Mean shift algorithm.
引文
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[13]王如意.变电站电力设备红外图像分割技术研究[D].西安:西安科技大学,2011.WANG Ruyi.Research of infrared segmentation for power equipment[D].Xi’an:Xidian University,2011.
[14]熊芬芳.基于图像处理技术的电气设备故障诊断方法[D].上海:东华大学,2015.XIONG Fenfang.Research on electrical equipments diagnosis methods based on image processing technology[D].Shanghai:Donghua University,2015.
[15]徐雪涛.基于红外成像技术的电气设备故障诊断[D].北京:华北电力大学,2014.XU Xuetao.Electrical equipment fault diagnosis based on infrared imaging technology[D].Beijing:North China Electric Power University,2014.
[16]MATAS J,CHUM O,URBAN M,et al.Robust wide-baseline stereo from maxmimally stable extremal regions[J].Image and Vision Computing,2004,22(10):761-767.
[17]MIKOLAJCZYK K,SCHMID C.A performance evaluation of local descriptors[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630.
[18]OH II-S,LEE J,MAJUMDER A.Multi-scale image segmentation using MSER[J].Lecture Notes in Computer Science,2013,8048(2):201-208.
[19]CHENG Yizong.Mean shift,mode seeking,and clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(8):790-799.
[20]COMANICIU D,MEER P.Mean shift:a robust approach toward feature space analysis[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2002,24:603-619.
[21]GONZALEZ R C,WOODS R E.Digital image processing[J].New Jersey:Prentice Hall,2007.
[22]GONZALEZ A,PEREZ J C,MUNOZ J,et al.Watershed image segmentation and cloud classification from multispectral MSG-SEVIRI imagery[J].Advances in Space Research,2012,49(1):135-142.
[23]ZHAN Kun,SHI Jinhui,WANG Haibo,et al.Computational mechanisms of pulse-coupled neural networks:a comprehensive review[J].Archives of Computational Methods in Engineering,2017,24(3):573-588.
[24]SUBASHINI M M,SAHOO S K.Pulse coupled neural networks and its applications[J].Expert Systems with Applications,2014,41:3965-3974.
[2]马洲俊,樊飞龙,王勇,等.基于多源异构数据的配电网故障信息挖掘与诊断[J].供用电,2018,35(8):31-39.MA Zhoujun,FAN Feilong,WANG Yong,et al.Distribution network fault information minning and diagnosis based on multi-source heterogeneous data[J].Distribution&Utilization,2018,35(8):31-39.
[3]罗飞,焦在滨,马钊,等.直流配电网故障分析和继电保护综述[J].供用电,2018,35(6):3-11.LUO Fei,JIAO Zaibin,MA Zhao,et al.Review of fault analysis and relay protection of DC distribution network[J].Distribution&Utilization,2018,35(6):3-11.
[4]王然,张志刚,孙保民,等.红外测温技术在炉膛温度场检测中的应用[J].热力发电,2017,46(6):136-140.WANG Ran,ZHANG Zhigang,SUN Baomin,et al.Application of infrared temperature detection technology in furnace temperature field measurement[J].Thermal Power Generation,2017,46(6):136-140.
[5]黄葆华,仇晓智,周卫庆,等.基于相似性建模和模糊概率有向图的热力设备故障诊断[J].热力发电,2018,47(4):104-109.HUANG Baohua,QIU Xiaozhi,ZHOU Weiqing,et al.Thermodynamic equipment fault diagnosis based on similarity modeling and fuzzy probabilistic signed directed graph[J].Thermal Power Generation,2018,47(4):104-109.
[6]黄新波,张杰,田毅,等.风电母线槽热故障状态在线监测系统[J].电力系统保护与控制,2017,45(24):131-157.HUANG Xinbo,ZHANG Jie,TIAN Yi,et al.On-line monitoring system for wind power busbar trunk heat fault status[J].Power System Protection and Control,2017,45(24):131-157.
[7]陈欢,彭辉,舒乃秋,等.基于鲁棒能量模型LS-TSVM和DGA的变压器故障诊断[J].电力系统保护与控制,2017,45(21):134-139.CHEN Huan,PENG Hui,SHU Naiqu,et al.Fault diagnosis of power transformer based on RELS-TSVMand DGA[J].Power System Protection and Control,2017,45(21):134-139.
[8]JAFFERY Z A,DUBEY A K.Design of early fault detection technique for electrical assets using infrared thermograms[J].International Journal of Electrical Power&Energy Systems,2014,63:753-759.
[9]甘锡淞,李云,傅成华,等.基于信息融合和CS-SVM的变压器绕组变形故障诊断方法研究[J].电力系统保护与控制,2018,43(4):156-161.GAN Xisong,LI Yun,FU Chenghua,et al.Information fusion and CS-SVM based research on diagnosis method for transformer winding deformation fault[J].Power System Protection and Control,2018,46(1):156-161.
[10]孙怡,吴邦树,刘仪卜,等.基于红外图像处理的电气设备故障诊断方法[J].机电工程技术,2016,46(1):58-61.SUN Yi,WU Bangshu,LIU Yibu,et al.Method of default diagnosing of electric equipments based on infrared image processing[J].Mechanical&Electrical,2016,45(6):58-61.
[11]井金剑,翟春艳,李书臣,等.基于红外图像识别的电气设备故障诊断[J].电子设计工程,2014,22(12):171-173.JING Jinjian,ZHAI Chunyan,LI Shuchen,et al.Fault diagnosis of electrical equipment based on infrared image recognition[J].Electronic Design Engineering,2014,22(12):171-173.
[12]门洪,于加学,秦蕾.基于CA和OTSU的电气设备红外图像分割方法[J].电力自动化设备,2011,31(9):93-95.MEN Hong,YU Jiaxue,QIN Lei.Segmentation of electric equipment infrared image based on CA and OTSU[J].Electric Power Automation Equipment,2011,31(9):93-95.
[13]王如意.变电站电力设备红外图像分割技术研究[D].西安:西安科技大学,2011.WANG Ruyi.Research of infrared segmentation for power equipment[D].Xi’an:Xidian University,2011.
[14]熊芬芳.基于图像处理技术的电气设备故障诊断方法[D].上海:东华大学,2015.XIONG Fenfang.Research on electrical equipments diagnosis methods based on image processing technology[D].Shanghai:Donghua University,2015.
[15]徐雪涛.基于红外成像技术的电气设备故障诊断[D].北京:华北电力大学,2014.XU Xuetao.Electrical equipment fault diagnosis based on infrared imaging technology[D].Beijing:North China Electric Power University,2014.
[16]MATAS J,CHUM O,URBAN M,et al.Robust wide-baseline stereo from maxmimally stable extremal regions[J].Image and Vision Computing,2004,22(10):761-767.
[17]MIKOLAJCZYK K,SCHMID C.A performance evaluation of local descriptors[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630.
[18]OH II-S,LEE J,MAJUMDER A.Multi-scale image segmentation using MSER[J].Lecture Notes in Computer Science,2013,8048(2):201-208.
[19]CHENG Yizong.Mean shift,mode seeking,and clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(8):790-799.
[20]COMANICIU D,MEER P.Mean shift:a robust approach toward feature space analysis[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2002,24:603-619.
[21]GONZALEZ R C,WOODS R E.Digital image processing[J].New Jersey:Prentice Hall,2007.
[22]GONZALEZ A,PEREZ J C,MUNOZ J,et al.Watershed image segmentation and cloud classification from multispectral MSG-SEVIRI imagery[J].Advances in Space Research,2012,49(1):135-142.
[23]ZHAN Kun,SHI Jinhui,WANG Haibo,et al.Computational mechanisms of pulse-coupled neural networks:a comprehensive review[J].Archives of Computational Methods in Engineering,2017,24(3):573-588.
[24]SUBASHINI M M,SAHOO S K.Pulse coupled neural networks and its applications[J].Expert Systems with Applications,2014,41:3965-3974.
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