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As a key component to ensure the efficient and safe operation of the transformer, the substation respirator’s performance and status have a direct impact on the overall safety of the power system. Aiming at the problem that the defect characteristics of respirators are diverse and unbalanced, this paper proposes a respirator defect generation method based on a regional attention mechanism and multi-scale features, called RM-GAN. First, based on the structural characteristics of the respirator, the model adopts a feature-preserving image preprocessing method and introduces a regional attention mechanism to improve the precise positioning and modeling of respirator components. Then, by combining the multi-scale features of the respirator in the discriminator, defect characteristics can be captured on respirators of different scales, thereby enhancing the accuracy and robustness of the generated results. Finally, experiments were conducted on a custom-built dataset of respirator defects to validate the effectiveness of RM-GAN. The results indicate that RM-GAN is capable of generating high-quality images of respirator defects.
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