Star Generative Adversarial VGG Network-Based Sample Augmentation for Insulator Defect Detection

Abstract Deep learning-based automated detection of insulator defects in electric power systems is a critical technological challenge, pivotal for ensuring reliability and efficiency in the global energy infrastructure. However, the effectiveness of the deep learning model is severely compromised by the scarcity of defective insulator samples. To tackle this problem, the present study proposes a style transfer approach utilizing an improved Star Generative Adversarial Network 2 (StarGAN2) model to generate artificial samples of faulty insulators, which adeptly synthesizes artificial faulty insulator samples on a one-to-many basis, markedly diminishing the necessity for extensive empirical data collection. Through the integration of identity loss, the proposed model ensures the fidelity of content and the preservation of critical defect semantics. Additionally, the proposed model incorporates a pre-trained Visual Geometry Group (VGG) network and perceptual loss, thus improving the quality of generated samples without additional artificial labeling. Finally, various experiments are conducted to assess the quality and authenticity of the generated samples and their impact on the detection model. The results demonstrate that StarGAN2 could generate realistic insulator defect samples and improve the performance of defect detection models.