Due to the high severity and low frequency of substation defects in expander topping and the small number of defect image samples

it is urgent to expand the small-sample defect images through image generation methods to support the effective improvement of substation defect image recognition algorithms. This paper proposes a small sample generation method for transformer defect images based on the improved diffusion model. Firstly

aiming at the problem that the generative model trained with small samples is difficult to control the authenticity of defects

an improved generative diffusion model is proposed. By adding the ResNext lightweight control branch

the precise generation of diverse defect details is achieved. Then

aiming at the problem of background distortion when generating large images and reducing the learning difficulty of the model and the video memory resources for generating large images

this paper proposes to generate through defect mask regions and fuse the defect regions into the original image. Finally

in view of the lack of a quality evaluation method for defect image generation

this paper comprehensively evaluates the quality of the generated defect images by integrating image quality indicators

including Frechet inception distance (FID)

inception score (IS)

structural similarity index measure (SSIM)

visual information fidelity(VIF)

and the improvement index of the target detection model (including F1 score). Experiments show that

compared with the GAN and SD series generative algorithms

the method proposed in this paper can be adopted to improve the image quality index of small-sample transformer defect image generation by 25%. Compared with the object detection algorithm trained only with real defects and normal samples

after adding normal images and the defect images generated using the method proposed in this paper

the F1 score of the trained object detection algorithm is increased by more than 12% on average.