Data augmentation method for insulators based on Cycle-GAN  

作　　者：Run Ye Azzedine Boukerche Xiao-Song Yu Cheng Zhang Bin Yan Xiao-Jia Zhou 

机构地区：[1]School of Automation Engineering,University of Electronic Science and Technology of China,Chengdu,611731,China [2]School of Electrical Engineering and Computer Science,University of Ottawa,Ottawa,K1N6N5,Canada [3]Yangtze River Delta Research Institute(Huzhou),University of Electronic Science and Technology of China,Huzhou,313001,China

出　　处：《Journal of Electronic Science and Technology》2024年第2期36-47,共12页电子科技学刊（英文版）

基　　金：supported in part by the National Natural Science Foundation of China under Grant No.61973055;Fundamental Research Funds for the Central Universities under Grant No.ZYGX2020J011;Regional Innovation Cooperation Funds of Sichuan under Grant No.2024YFHZ0089.

摘　　要：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.

关 键 词：Data expansion Deep learning Generate confrontation network INSULATOR 

分 类 号：TM63[电气工程—电力系统及自动化] TP311.13[自动化与计算机技术—计算机软件与理论]