Data Augmentation Techniques of Power Facilities for Improve Deep Learning Performance

• Published in: KEPCO Journal on electric power and energy Vol. 7; no. 2; pp. 323 - 328
• Main Authors: 장승민, 손승우, 김봉석, Jang, Seungmin, Son, Seungwoo, Kim, Bongsuck
• Format: Journal Article
• Language: Korean
• Published: 2021
• Subjects: Generative Adversarial Network, GAN; Transfer Learning; Visual Intelligence
• ISSN: 2465-8111; 2466-0124

Diagnostic models are required. Data augmentation is one of the best ways to improve deep learning performance. Traditional augmentation techniques that modify image brightness or spatial information are difficult to achieve great results. To overcome this, a generative adversarial network (GAN) technology that generates virtual data to increase deep learning performance has emerged. GAN can create realistic-looking fake images by competitive learning two networks, a generator that creates fakes and a discriminator that determines whether images are real or fake made by the generator. GAN is being used in computer vision, IT solutions, and medical imaging fields. It is essential to secure additional learning data to advance deep learning-based fault diagnosis solutions in the power industry where facilities are strictly maintained more than other industries. In this paper, we propose a method for generating power facility images using GAN and a strategy for improving performance when only used a small amount of data. Finally, we analyze the performance of the augmented image to see if it could be utilized for the deep learning-based diagnosis system or not.