A few-shot image generation method for power defect scenarios

Abstract

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 employed. 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 generation 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 unavailability in power defect scenarios.

Keywords

• few-shot image generation
• power defect
• context-aware
• lc-divergence