Abstract: In the operation and maintenance of power systems,timely and accurate detection of power defects is crucial to ensure the safety and stability of the system.However,due to the difficulty in obtaining image data of power defect scenes,deep learning models often face the problem of insufficient training samples.To solve this problem,this paper applies the diffusion model to power defect image generation and proposes a few-shot generative adaptation method based on texture modulation and EMA parameter update to expand the power defect image dataset.Specifically,this paper introduces a texture modulation module into the diffusion model,and improves the image’s detail capture ability and spatial structure alignment ability through a two-stage injection mechanism.In addition,this paper designs a cross-domain adaptive training strategy for EMA parameter update,which combines style loss and diffusion loss to smooth the model training process and improve the quality and stability of generated images.Experimental results show that this method performs well on multiple few-shot datasets of power equipment defects,and the ge-nerated images have high spatial structure consistency and detail restoration capabilities,showing its application potential in power defect detection.

Key words: Power defect, Few-shot image generation, Generative adaptation, Diffusion model, Texture modulation, Exponential moving average