May 2017

Article Contents

Chinese Optics> 2017> 10(3): 340-347.

HUANG Bo, JIANG Shen-wang, ZHANG Zeng, ZHANG Jin, ZHANG Wei, XU Ting-fa. Intelligent identification algorithm of adaptive feature drainage tube fault[J]. Chinese Optics, 2017, 10(3): 340-347. doi: 10.3788/CO.20171003.0340

Citation:

HUANG Bo, JIANG Shen-wang, ZHANG Zeng, ZHANG Jin, ZHANG Wei, XU Ting-fa. Intelligent identification algorithm of adaptive feature drainage tube fault[J].Chinese Optics, 2017, 10(3): 340-347.doi:10.3788/CO.20171003.0340

Citation:

HUANG Bo, JIANG Shen-wang, ZHANG Zeng, ZHANG Jin, ZHANG Wei, XU Ting-fa. Intelligent identification algorithm of adaptive feature drainage tube fault[J].Chinese Optics, 2017, 10(3): 340-347.doi:10.3788/CO.20171003.0340

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Intelligent identification algorithm of adaptive feature drainage tube fault

doi:10.3788/CO.20171003.0340

1.
Key Laboratory of Optoelectronic Imaging System and Technology, Ministry of Education, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China
2.
Tianjin Aerospace Science and Technology Co., Ltd. for the Data System, Tianjin 300301, China
3.
Southern Power Grid Science Research Institute Co., Ltd, Guangzhou 510080, China

Funds:

Helicopter Major Project of China Southern Power GridK-KY2014-500

Received Date:19 Jan 2017
Rev Recd Date:27 Mar 2017
Publish Date:01 Jun 2017

Abstract

Abstract

In this paper, an intelligent recognition algorithm for hidden danger of drainage tube is presented in order to realize the automatic detection of the faults of the high voltage transmission line. First, the thermal image feature of faults is analyzed, and the faults can be divided into two types:obvious heating and weak heating. Second in view of the obvious heating caused by the drainage tube, the improved Ostu threshold segmentation method is used to implement infrared image segmentation and the improved Sobel operator is used to implment contour extraction. Third, the seed filling algorithm separation is used to connect domains, and we can determine whether the drainage tube is fault through the thread characteristics. Finally, we check the weak heating caused by the drainage tube, applying high pressure transmission line parallel features to find the region of trunk line, and then get the Harris corner around the trunk region and determine whether it is fault drainage through the STWN characteristics. Experimental results show that the successful identification rate of hidden heat fault is 94.6%, false negative rate is 2.2%, and false recognition rate is 5.5%.
- infrared thermal image,
- boundary development,
- morphological feature,
- intelligent recognition

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References (20)

References

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