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Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network
The belt conveyor is an essential piece of equipment in coal mining for coal transportation, and its stable operation is key to efficient production. Belt surface of the conveyor is vulnerable to foreign bodies which can be extremely destructive. In the past decades, much research and numerous appro...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101271/ https://www.ncbi.nlm.nih.gov/pubmed/35591175 http://dx.doi.org/10.3390/s22093485 |
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author | Guo, Xiaoqiang Liu, Xinhua Królczyk, Grzegorz Sulowicz, Maciej Glowacz, Adam Gardoni, Paolo Li, Zhixiong |
author_facet | Guo, Xiaoqiang Liu, Xinhua Królczyk, Grzegorz Sulowicz, Maciej Glowacz, Adam Gardoni, Paolo Li, Zhixiong |
author_sort | Guo, Xiaoqiang |
collection | PubMed |
description | The belt conveyor is an essential piece of equipment in coal mining for coal transportation, and its stable operation is key to efficient production. Belt surface of the conveyor is vulnerable to foreign bodies which can be extremely destructive. In the past decades, much research and numerous approaches to inspect belt status have been proposed, and machine learning-based non-destructive testing (NDT) methods are becoming more and more popular. Deep learning (DL), as a branch of machine learning (ML), has been widely applied in data mining, natural language processing, pattern recognition, image processing, etc. Generative adversarial networks (GAN) are one of the deep learning methods based on generative models and have been proved to be of great potential. In this paper, a novel multi-classification conditional CycleGAN (MCC-CycleGAN) method is proposed to generate and discriminate surface images of damages of conveyor belt. A novel architecture of improved CycleGAN is designed to enhance the classification performance using a limited capacity images dataset. Experimental results show that the proposed deep learning network can generate realistic belt surface images with defects and efficiently classify different damaged images of the conveyor belt surface. |
format | Online Article Text |
id | pubmed-9101271 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91012712022-05-14 Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network Guo, Xiaoqiang Liu, Xinhua Królczyk, Grzegorz Sulowicz, Maciej Glowacz, Adam Gardoni, Paolo Li, Zhixiong Sensors (Basel) Article The belt conveyor is an essential piece of equipment in coal mining for coal transportation, and its stable operation is key to efficient production. Belt surface of the conveyor is vulnerable to foreign bodies which can be extremely destructive. In the past decades, much research and numerous approaches to inspect belt status have been proposed, and machine learning-based non-destructive testing (NDT) methods are becoming more and more popular. Deep learning (DL), as a branch of machine learning (ML), has been widely applied in data mining, natural language processing, pattern recognition, image processing, etc. Generative adversarial networks (GAN) are one of the deep learning methods based on generative models and have been proved to be of great potential. In this paper, a novel multi-classification conditional CycleGAN (MCC-CycleGAN) method is proposed to generate and discriminate surface images of damages of conveyor belt. A novel architecture of improved CycleGAN is designed to enhance the classification performance using a limited capacity images dataset. Experimental results show that the proposed deep learning network can generate realistic belt surface images with defects and efficiently classify different damaged images of the conveyor belt surface. MDPI 2022-05-03 /pmc/articles/PMC9101271/ /pubmed/35591175 http://dx.doi.org/10.3390/s22093485 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 Guo, Xiaoqiang Liu, Xinhua Królczyk, Grzegorz Sulowicz, Maciej Glowacz, Adam Gardoni, Paolo Li, Zhixiong Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network |
title | Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network |
title_full | Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network |
title_fullStr | Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network |
title_full_unstemmed | Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network |
title_short | Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network |
title_sort | damage detection for conveyor belt surface based on conditional cycle generative adversarial network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101271/ https://www.ncbi.nlm.nih.gov/pubmed/35591175 http://dx.doi.org/10.3390/s22093485 |
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