基于红外与紫外图像信息融合的绝缘子污秽状态识别

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Abstract In order to realize the non-contact measurement of insulator pollution severity, a method based on decision level fusion of IR and UV image information is proposed. Features of IR and UV images are calculated separately. Meanwhile, feature selection based on Fisher criterion is carried out to obtain features which have the ability to represent the contamination grades efficiently. In order to improve the calculation speed and precision of classifier, Kernel principal component analysis (KPCA) is adopted to extract three-dimensional Kernel principal features of IR and UV images. Radial basis function neural network(RBFNN) is used to identify the contamination grades using IR and UV features separately. And then, D-S theory is adopted to achieve the decision fusion and realize the high accuracy identification of contamination grades. Results of the experiments indicate that the precision of proposed method is significantly superior to recognition using IR or UV features separately. This paper provides a new method for the prevention of pollution flashover.

Key words： Contamination grades decision fusion Fisher criterion Kernel principal component analysis radial basis function neural network

Received: 16 September 2013 Published: 05 November 2014

PACS:

TP391.4

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	Articles by authors
	Jin Lijun
	Zhang Da
	Duan Shaohui
	Yao Senjing

Cite this article:

Jin Lijun,Zhang Da,Duan Shaohui等. Recognition of Contamination Grades of Insulators Based on IR and UV Image Information Fusion[J]. Transactions of China Electrotechnical Society, 2014, 29(8): 309-318.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2014/V29/I8/309

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