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

Na Zhang (), Jingjing Fan, Gang Yang, Guodong Li, Hong Yang and Yang Bai
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Na Zhang: State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China
Jingjing Fan: State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China
Gang Yang: State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China
Guodong Li: State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China
Hong Yang: State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China
Yang Bai: State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China

Mathematics, 2024, vol. 12, issue 18, 1-17

Abstract: 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.

Keywords: augmentation of blemish images in substations; YOLO; GAN; localized area blemish image generation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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