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IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global–Local Fusion

Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data to achieve ideal detection effect results. Power...

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Autores principales: Chen, Yi, Yan, Yunfeng, Wang, Xianbo, Zheng, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383857/
https://www.ncbi.nlm.nih.gov/pubmed/37514825
http://dx.doi.org/10.3390/s23146531
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author Chen, Yi
Yan, Yunfeng
Wang, Xianbo
Zheng, Yi
author_facet Chen, Yi
Yan, Yunfeng
Wang, Xianbo
Zheng, Yi
author_sort Chen, Yi
collection PubMed
description Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data to achieve ideal detection effect results. Power scene data involve privacy and security issues, and there is an imbalance in the number of samples across different defect categories, all of which will affect the performance of defect detection models. With the emergence of the Internet of Things (IoT), the integration of IoT with machine learning offers a new direction for defect detection in power equipment. Meanwhile, a generative adversarial network based on multi-view fusion and self-attention is proposed for few-shot image generation, named MVSA-GAN. The IoT devices capture real-time data from the power scene, which are then used to train the MVSA-GAN model, enabling it to generate realistic and diverse defect data. The designed self-attention encoder focuses on the relevant features of different parts of the image to capture the contextual information of the input image and improve the authenticity and coherence of the image. A multi-view feature fusion module is proposed to capture the complex structure and texture of the power scene through the selective fusion of global and local features, and improve the authenticity and diversity of generated images. Experiments show that the few-shot image generation method proposed in this paper can generate real and diverse defect data for power scene defects. The proposed method achieved FID and LPIPS scores of 67.87 and 0.179, surpassing SOTA methods, such as FIGR and DAWSON.
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spelling pubmed-103838572023-07-30 IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global–Local Fusion Chen, Yi Yan, Yunfeng Wang, Xianbo Zheng, Yi Sensors (Basel) Article Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data to achieve ideal detection effect results. Power scene data involve privacy and security issues, and there is an imbalance in the number of samples across different defect categories, all of which will affect the performance of defect detection models. With the emergence of the Internet of Things (IoT), the integration of IoT with machine learning offers a new direction for defect detection in power equipment. Meanwhile, a generative adversarial network based on multi-view fusion and self-attention is proposed for few-shot image generation, named MVSA-GAN. The IoT devices capture real-time data from the power scene, which are then used to train the MVSA-GAN model, enabling it to generate realistic and diverse defect data. The designed self-attention encoder focuses on the relevant features of different parts of the image to capture the contextual information of the input image and improve the authenticity and coherence of the image. A multi-view feature fusion module is proposed to capture the complex structure and texture of the power scene through the selective fusion of global and local features, and improve the authenticity and diversity of generated images. Experiments show that the few-shot image generation method proposed in this paper can generate real and diverse defect data for power scene defects. The proposed method achieved FID and LPIPS scores of 67.87 and 0.179, surpassing SOTA methods, such as FIGR and DAWSON. MDPI 2023-07-19 /pmc/articles/PMC10383857/ /pubmed/37514825 http://dx.doi.org/10.3390/s23146531 Text en © 2023 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, Yi
Yan, Yunfeng
Wang, Xianbo
Zheng, Yi
IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global–Local Fusion
title IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global–Local Fusion
title_full IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global–Local Fusion
title_fullStr IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global–Local Fusion
title_full_unstemmed IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global–Local Fusion
title_short IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global–Local Fusion
title_sort iot-enabled few-shot image generation for power scene defect detection based on self-attention and global–local fusion
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383857/
https://www.ncbi.nlm.nih.gov/pubmed/37514825
http://dx.doi.org/10.3390/s23146531
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