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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10575288 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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