Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 581-586.doi: 10.11896/jsjkx.200500026

• Interdiscipline & Application • Previous Articles     Next Articles

Double-cycle Consistent Insulator Defect Sample Generation Method Based on Local Fine-grainedInformation Guidance

ZHAO Xiao1, LI Shi-lin2, LI Fan3, YU Zheng-tao3, ZHANG Lin-hua1, YANG Yong2   

  1. 1 Chuxiong Power Supply Bureau of Yunnan Power Grid Co.,Ltd.Chuxiong,Yunnan 675000,China
    2 Eleictric Power Reasearch Institute of Yunnan Power Grid Co.,Ltd.,Kunming 650217,China
    3 Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:ZHAO Xiao,born in 1982,undergraduate,engineer.His main research interests include transmission line operation and maintenance.
    LI Fan,born in 1986,Ph.D,lecturer,master's supervisor,is a member of China Computer Federation.His main research interests include picture processing and image recognition
  • Supported by:
    National Key R&D Program of China(2018YFC0830105,2018YFC0830100) and Science and Technology Project of Yunnan Power Grid Company(YNKJXM20190729).

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Abstract: In view of the lack of data for insulator defects samples,the existing generation methods require a large number of training samples,and the details of insulator defects are often lost or distorted during the generation process.This paper presents a double-cycle consistent insulator defect sample generation method based on local fine-grained information guidance (LFGI-DCC).The approach uses the rough insulator image as the network input,and learns from the fine defect insulator sample through the cycle consistency generative adversarial method to generate more realistic defect insulator samples.At the same time,the image of the defect area in the generated image is used as the input to discriminator,and the generation network is guided to focus on the fine-grained information of the defect by the method of resisting constraints,thereby further improving the authenti-city and diversity of the insulator defect samples.Compared with the existing methods,the insulator defect dataset constructed by the proposed method has the characteristics of fidelity and diversification,which provides an important foundation for improving the accuracy of insulator defect automatic identification.

Key words: Adversarial network, Double-cycle consistency, Insulator, Local fine-grained information, Sample generation

CLC Number: 

  • TP183
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