A Novel Method for Localized Typical Blemish Image Data Generation in Substations

Current mainstream methods for detecting surface blemishes on substation equipment typically rely on extensive sets of blemish images for training. However, the unpredictable nature and infrequent occurrence of such blemishes present significant challenges in data collection. To tackle these issues, this paper proposes a novel approach for generating localized, representative blemish images within substations. Firstly, to mitigate global style variations in images generated by generative adversarial networks (GANs), we developed a YOLO-LRD method focusing on local region detection within equipment. This method enables precise identification of blemish locations in substation equipment images. Secondly, we introduce a SEB-GAN model tailored specifically for generating blemish images within substations. By confining blemish generation to identified regions within equipment images, the authenticity and diversity of the generated defect data are significantly enhanced. Theexperimental results validate that the YOLO-LRD and SEB-GAN techniques effectively create precise datasets depicting flaws in substations.