UAV Aerial Image Generation of Crucial Components of High-Voltage Transmission Lines Based on Multi-Level Generative Adversarial Network

Author:

Wang Jinyu12,Li Yingna12,Chen Wenxiang12

Affiliation:

1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

2. Computer Technology Application Key Lab of the Yunnan Province, Kunming 650500, China

Abstract

With the aim of improving the image quality of the crucial components of transmission lines taken by unmanned aerial vehicles (UAV), a priori work on the defective fault location of high-voltage transmission lines has attracted great attention from researchers in the UAV field. In recent years, generative adversarial nets (GAN) have achieved good results in image generation tasks. However, the generation of high-resolution images with rich semantic details from complex backgrounds is still challenging. Therefore, we propose a novel GANs-based image generation model to be used for the critical components of power lines. However, to solve the problems related to image backgrounds in public data sets, considering that the image background of the common data set CPLID (Chinese Power Line Insulator Dataset) is simple. However, it cannot fully reflect the complex environments of transmission line images; therefore, we established an image data set named “KCIGD” (The Key Component Image Generation Dataset), which can be used for model training. CFM-GAN (GAN networks based on coarse–fine-grained generators and multiscale discriminators) can generate the images of the critical components of transmission lines with rich semantic details and high resolutions. CFM-GAN can provide high-quality image inputs for transmission line fault detection and line inspection models to guarantee the safe operation of power systems. Additionally, we can use these high-quality images to expand the data set. In addition, CFM-GAN consists of two generators and multiple discriminators, which can be flexibly applied to image generation tasks in other scenarios. We introduce a penalty mechanism-related Monte Carlo search (MCS) approach in the CFM-GAN model to introduce more semantic details in the generated images. Moreover, we presented a multiscale discriminator structure according to the multitask learning mechanisms to effectively enhance the quality of the generated images. Eventually, the experiments using the CFM-GAN model on the KCIGD dataset and the publicly available CPLID indicated that the model used in this work outperformed existing mainstream models in improving image resolution and quality.

Funder

National Natural Science Foundation of China

Applied Basic Research Project of Yunnan province

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference51 articles.

1. DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases;Tayal;Multimed. Syst.,2021

2. Saravanababu, K., Balakrishnan, P., and Sathiyasekar, K. (2013, January 6–8). Transmission line faults detection, classification, and location using Discrete Wavelet Transform. Proceedings of the International Conference on Power, Energy and Control (ICPEC), Dindigul, India.

3. Zhang, Y., Yuan, X., Li, W., and Chen, S. (2017). Automatic Power Line Inspection Using UAV Images. Remote Sens., 9.

4. Larochelle, H., and Murray, I. (2011, January 11–13). The neural autoregressive distribution estimator. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA. JMLR Workshop and Conference Proceedings.

5. Kingma, D.P., and Welling, M. (2013). Auto-Encoding Variational Bayes [EB/OL]. arXiv.

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3