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Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model
Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, w...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948749/ https://www.ncbi.nlm.nih.gov/pubmed/29614813 http://dx.doi.org/10.3390/s18041064 |
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author | Mei, Shuang Wang, Yudan Wen, Guojun |
author_facet | Mei, Shuang Wang, Yudan Wen, Guojun |
author_sort | Mei, Shuang |
collection | PubMed |
description | Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates. |
format | Online Article Text |
id | pubmed-5948749 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-59487492018-05-17 Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model Mei, Shuang Wang, Yudan Wen, Guojun Sensors (Basel) Article Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates. MDPI 2018-04-02 /pmc/articles/PMC5948749/ /pubmed/29614813 http://dx.doi.org/10.3390/s18041064 Text en © 2018 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mei, Shuang Wang, Yudan Wen, Guojun Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model |
title | Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model |
title_full | Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model |
title_fullStr | Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model |
title_full_unstemmed | Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model |
title_short | Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model |
title_sort | automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948749/ https://www.ncbi.nlm.nih.gov/pubmed/29614813 http://dx.doi.org/10.3390/s18041064 |
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