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An Overview of Image Generation of Industrial Surface Defects

Intelligent defect detection technology combined with deep learning has gained widespread attention in recent years. However, the small number, and diverse and random nature, of defects on industrial surfaces pose a significant challenge to deep learning-based methods. Generating defect images can e...

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Detalles Bibliográficos
Autores principales: Zhong, Xiaopin, Zhu, Junwei, Liu, Weixiang, Hu, Chongxin, Deng, Yuanlong, Wu, Zongze
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575288/
https://www.ncbi.nlm.nih.gov/pubmed/37836990
http://dx.doi.org/10.3390/s23198160
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author Zhong, Xiaopin
Zhu, Junwei
Liu, Weixiang
Hu, Chongxin
Deng, Yuanlong
Wu, Zongze
author_facet Zhong, Xiaopin
Zhu, Junwei
Liu, Weixiang
Hu, Chongxin
Deng, Yuanlong
Wu, Zongze
author_sort Zhong, Xiaopin
collection PubMed
description Intelligent defect detection technology combined with deep learning has gained widespread attention in recent years. However, the small number, and diverse and random nature, of defects on industrial surfaces pose a significant challenge to deep learning-based methods. Generating defect images can effectively solve this problem. This paper investigates and summarises traditional defect generation and deep learning-based methods. It analyses the various advantages and disadvantages of these methods and establishes a benchmark through classical adversarial networks and diffusion models. The performance of these methods in generating defect images is analysed through various indices. This paper discusses the existing methods, highlights the shortcomings and challenges in the field of defect image generation, and proposes future research directions. Finally, the paper concludes with a summary.
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spelling pubmed-105752882023-10-14 An Overview of Image Generation of Industrial Surface Defects Zhong, Xiaopin Zhu, Junwei Liu, Weixiang Hu, Chongxin Deng, Yuanlong Wu, Zongze Sensors (Basel) Review Intelligent defect detection technology combined with deep learning has gained widespread attention in recent years. However, the small number, and diverse and random nature, of defects on industrial surfaces pose a significant challenge to deep learning-based methods. Generating defect images can effectively solve this problem. This paper investigates and summarises traditional defect generation and deep learning-based methods. It analyses the various advantages and disadvantages of these methods and establishes a benchmark through classical adversarial networks and diffusion models. The performance of these methods in generating defect images is analysed through various indices. This paper discusses the existing methods, highlights the shortcomings and challenges in the field of defect image generation, and proposes future research directions. Finally, the paper concludes with a summary. MDPI 2023-09-28 /pmc/articles/PMC10575288/ /pubmed/37836990 http://dx.doi.org/10.3390/s23198160 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 Review
Zhong, Xiaopin
Zhu, Junwei
Liu, Weixiang
Hu, Chongxin
Deng, Yuanlong
Wu, Zongze
An Overview of Image Generation of Industrial Surface Defects
title An Overview of Image Generation of Industrial Surface Defects
title_full An Overview of Image Generation of Industrial Surface Defects
title_fullStr An Overview of Image Generation of Industrial Surface Defects
title_full_unstemmed An Overview of Image Generation of Industrial Surface Defects
title_short An Overview of Image Generation of Industrial Surface Defects
title_sort overview of image generation of industrial surface defects
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575288/
https://www.ncbi.nlm.nih.gov/pubmed/37836990
http://dx.doi.org/10.3390/s23198160
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