局部细粒度信息引导的双循环一致性绝缘子缺陷样本生成

doi:10.11896/jsjkx.200500026

计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 581-586.doi: 10.11896/jsjkx.200500026

局部细粒度信息引导的双循环一致性绝缘子缺陷样本生成

赵潇¹, 李仕林², 李凡³, 余正涛³, 张林华¹, 杨勇²

1 云南电网有限责任公司楚雄供电局云南楚雄675000
2 云南电网有限责任公司电力科学研究院昆明650217
3 昆明理工大学信息工程与自动化学院昆明650500

出版日期:2021-06-10 发布日期:2021-06-17
通讯作者: 李凡(478263823@qq.com)
作者简介:81878618@qq.com
基金资助:
国家重点研发计划项目(2018YFC0830105,2018YFC0830100);云南电网公司科技项目(YNKJXM20190729)

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 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).

摘要/Abstract

摘要： 针对绝缘子缺陷样本数据缺乏,现有生成方法又要求训练样本的规模庞大,且在生成过程中绝缘子缺陷的细节常常被丢失或扭曲,提出了一种基于局部细粒度信息引导的双循环一致性绝缘子缺陷样本生成方法。该方法利用粗糙绝缘子图像作为网络输入,提出通过循环一致性生成对抗方法向精细缺陷绝缘子样本学习,生成较为逼真的缺陷样本。为使生成的样本具有丰富的缺陷特征,提出将生成图像中的缺陷区域图像作为判别网络的输入,并利用对抗约束的方式引导生成网络重点关注缺陷的细粒度信息,从而进一步提升生成绝缘子缺陷样本的真实性和多样性。与现有方法相比,所提方法构建的绝缘子缺陷样本数据集具有逼真、多样化等特点,为提升绝缘子缺陷自动识别的准确性提供了重要的数据基础。

关键词: 对抗式网络, 局部细粒度信息, 绝缘子, 双循环一致性, 样本生成

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

中图分类号:

TP183

赵潇, 李仕林, 李凡, 余正涛, 张林华, 杨勇. 局部细粒度信息引导的双循环一致性绝缘子缺陷样本生成[J]. 计算机科学, 2021, 48(6A): 581-586. https://doi.org/10.11896/jsjkx.200500026

ZHAO Xiao, LI Shi-lin, LI Fan, YU Zheng-tao, ZHANG Lin-hua, YANG Yong. Double-cycle Consistent Insulator Defect Sample Generation Method Based on Local Fine-grainedInformation Guidance[J]. Computer Science, 2021, 48(6A): 581-586. https://doi.org/10.11896/jsjkx.200500026

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局部细粒度信息引导的双循环一致性绝缘子缺陷样本生成

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

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