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Intelligent Recognition Method of Composite Insulator Hydrophobicity Based on Image Processing
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Qiuyu YANG1, Xiaogang ZHENG1, Jianxing LI1, Liwei LIN1, Yun HAN2
Insulating Materials | 2022, 55(9) : 100 - 106
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Insulating Materials | 2022, 55(9): 100-106
• Test and Analysis •
Intelligent Recognition Method of Composite Insulator Hydrophobicity Based on Image Processing
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Qiuyu YANG1, Xiaogang ZHENG1, Jianxing LI1, Liwei LIN1, Yun HAN2
Affiliations
  • 1School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
  • 2Fujian CQEPOWER Electric Co., Ltd., Fuzhou 350109, China
Published: 2022-09-20 doi: 10.16790/j.cnki.1009-9239.im.2022.09.017
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In this paper, we proposed an intelligent recognition method for composite insulator hydrophobicity on the basis of image processing. For the composite insulator images with different hydrophobicity levels, the images were performed histogram equalization and filtering treatments at first, then the water drop/water trace was separated from the background by the Otsu threshold segmentation method, so as to extract clear and complete water drop/water trace contour. The water drop/water trace coverage rate, the maximum water drop/water trace area ratio, and the water drop/water trace average size were used to quantify the water drop/water trace, and the support vector machine was used to establish the feature classification model, so as to realize the hydrophobicity intelligent recognition of composite insulators. The results show that the hydrophobicity intelligent identification method of composite insulator based on image processing can effectively identify seven kinds of hydrophobicity levels, and the average identification accuracy rate is above 80%.

composite insulator  /  hydrophobicity  /  image processing  /  intelligent identification
Qiuyu YANG, Xiaogang ZHENG, Jianxing LI, Liwei LIN, Yun HAN. Intelligent Recognition Method of Composite Insulator Hydrophobicity Based on Image Processing[J]. Insulating Materials, 2022 , 55 (9) : 100 -106 . DOI: 10.16790/j.cnki.1009-9239.im.2022.09.017
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doi: 10.16790/j.cnki.1009-9239.im.2022.09.017
  • Receive Date：2021-09-23
  • Online Date：2025-12-23
  • Published：2022-09-20
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  • Received：2021-09-23
  • Revised：2021-11-29
Affiliations
    1School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
    2Fujian CQEPOWER Electric Co., Ltd., Fuzhou 350109, China
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表12种不同金属材料的力学参数
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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