首页|Data augmentation method for insulators based on Cycle-GAN

Data augmentation method for insulators based on Cycle-GAN

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
Data augmentation is an important task of using existing data to expand data sets.Using generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples,simple training,and fewer restrictions on the number of generated samples.However,in the field of transmission line insulator images,the freely synthesized samples are prone to produce fuzzy backgrounds and disordered samples of the main insulator features.To solve the above problems,this paper uses the cycle generative adversarial network(Cycle-GAN)used for domain conversion in the generation countermeasure network as the initial framework and uses the self-attention mechanism and channel attention mechanism to assist the conversion to realize the mutual conversion of different insulator samples.The attention module with prior knowledge is used to build the generation countermeasure network,and the generative adversarial network(GAN)model with local controllable generation is built to realize the directional generation of insulator belt defect samples.The experimental results show that the samples obtained by this method are improved in a number of quality indicators,and the quality effect of the samples obtained is excellent,which has a reference value for the data expansion of insulator images.

Data expansionDeep learningGenerate confrontation networkInsulator

Run Ye、Azzedine Boukerche、Xiao-Song Yu、Cheng Zhang、Bin Yan、Xiao-Jia Zhou

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School of Automation Engineering,University of Electronic Science and Technology of China,Chengdu,611731,China

Yangtze River Delta Research Institute(Huzhou),University of Electronic Science and Technology of China,Huzhou,313001,China

School of Electrical Engineering and Computer Science,University of Ottawa,Ottawa,K1N6N5,Canada

School of Automation Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China

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2024

10.1016/j.jnlest.2024.100250

电子科技学刊
电子科技大学

电子科技学刊

影响因子：0.154
ISSN：1674-862X
年,卷(期)：2024.22(2)