Due to the limited availability of power defect data,most current defect detection methods are unable to accurately detect power system anomalies.To overcome this challenge,a few-shot image generation method is em-ployed.Building upon the improved local-fusion generative adversarial network(LoFGAN),a context-aware few-shot image generator is designed to enhance the defect detection network's capability to extract detailed features.A regularization loss based on LC-divergence is introduced to optimize the training effectiveness of the image genera-tion model on limited datasets.Experimental results reveal that the few-shot image generation method can generate effective and diverse defect data for power scenarios.The proposed model can address the issue of data unavailabil-ity in power defect scenarios.
国家科技期刊平台
NSTL
维普
万方数据
