Cargando…

Transmission Line Vibration Damper Detection Using Multi-Granularity Conditional Generative Adversarial Nets Based on UAV Inspection Images

The vibration dampers can eliminate the galloping phenomenon of transmission lines caused by the wind. The detection of vibration dampers based on visual technology is an important issue. Current CNN-based methods struggle to meet the requirements of real-time detection. Therefore, the current vibra...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Wenxiang, Li, Yingna, Zhao, Zhengang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914797/
https://www.ncbi.nlm.nih.gov/pubmed/35271033
http://dx.doi.org/10.3390/s22051886
_version_ 1784667835724726272
author Chen, Wenxiang
Li, Yingna
Zhao, Zhengang
author_facet Chen, Wenxiang
Li, Yingna
Zhao, Zhengang
author_sort Chen, Wenxiang
collection PubMed
description The vibration dampers can eliminate the galloping phenomenon of transmission lines caused by the wind. The detection of vibration dampers based on visual technology is an important issue. Current CNN-based methods struggle to meet the requirements of real-time detection. Therefore, the current vibration damper detection work has mainly been carried out manually. In view of the above situation, we propose a vibration damper detection-image generation model called DamperGAN based on multi-granularity Conditional Generative Adversarial Nets. DamperGAN first generates a low-resolution detection result image based on a coarse-grained module, then uses Monte Carlo search to mine the latent information in the low-resolution image, and finally injects this information into a fine-grained module through an attention mechanism to output high-resolution images and penalize poor intermediate information. At the same time, we propose a multi-level discriminator based on the multi-task learning mechanism to improve the discriminator’s discriminative ability and promote the generator to output better images. Finally, experiments on the self-built DamperGenSet dataset show that the images generated by our model are superior to the current mainstream baselines in both resolution and quality.
format Online
Article
Text
id pubmed-8914797
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-89147972022-03-12 Transmission Line Vibration Damper Detection Using Multi-Granularity Conditional Generative Adversarial Nets Based on UAV Inspection Images Chen, Wenxiang Li, Yingna Zhao, Zhengang Sensors (Basel) Article The vibration dampers can eliminate the galloping phenomenon of transmission lines caused by the wind. The detection of vibration dampers based on visual technology is an important issue. Current CNN-based methods struggle to meet the requirements of real-time detection. Therefore, the current vibration damper detection work has mainly been carried out manually. In view of the above situation, we propose a vibration damper detection-image generation model called DamperGAN based on multi-granularity Conditional Generative Adversarial Nets. DamperGAN first generates a low-resolution detection result image based on a coarse-grained module, then uses Monte Carlo search to mine the latent information in the low-resolution image, and finally injects this information into a fine-grained module through an attention mechanism to output high-resolution images and penalize poor intermediate information. At the same time, we propose a multi-level discriminator based on the multi-task learning mechanism to improve the discriminator’s discriminative ability and promote the generator to output better images. Finally, experiments on the self-built DamperGenSet dataset show that the images generated by our model are superior to the current mainstream baselines in both resolution and quality. MDPI 2022-02-28 /pmc/articles/PMC8914797/ /pubmed/35271033 http://dx.doi.org/10.3390/s22051886 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Wenxiang
Li, Yingna
Zhao, Zhengang
Transmission Line Vibration Damper Detection Using Multi-Granularity Conditional Generative Adversarial Nets Based on UAV Inspection Images
title Transmission Line Vibration Damper Detection Using Multi-Granularity Conditional Generative Adversarial Nets Based on UAV Inspection Images
title_full Transmission Line Vibration Damper Detection Using Multi-Granularity Conditional Generative Adversarial Nets Based on UAV Inspection Images
title_fullStr Transmission Line Vibration Damper Detection Using Multi-Granularity Conditional Generative Adversarial Nets Based on UAV Inspection Images
title_full_unstemmed Transmission Line Vibration Damper Detection Using Multi-Granularity Conditional Generative Adversarial Nets Based on UAV Inspection Images
title_short Transmission Line Vibration Damper Detection Using Multi-Granularity Conditional Generative Adversarial Nets Based on UAV Inspection Images
title_sort transmission line vibration damper detection using multi-granularity conditional generative adversarial nets based on uav inspection images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914797/
https://www.ncbi.nlm.nih.gov/pubmed/35271033
http://dx.doi.org/10.3390/s22051886
work_keys_str_mv AT chenwenxiang transmissionlinevibrationdamperdetectionusingmultigranularityconditionalgenerativeadversarialnetsbasedonuavinspectionimages
AT liyingna transmissionlinevibrationdamperdetectionusingmultigranularityconditionalgenerativeadversarialnetsbasedonuavinspectionimages
AT zhaozhengang transmissionlinevibrationdamperdetectionusingmultigranularityconditionalgenerativeadversarialnetsbasedonuavinspectionimages