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Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network

Owing to the recent development in deep learning, machine vision has been widely used in intelligent manufacturing equipment in multiple fields, including precision-manufacturing production lines and online product-quality inspection. This study aims at online Machine Vision Inspection, focusing on...

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Detalles Bibliográficos
Autores principales: Huang, Jian, Guixiong, Liu, He, Binyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875430/
https://www.ncbi.nlm.nih.gov/pubmed/33566844
http://dx.doi.org/10.1371/journal.pone.0246093
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author Huang, Jian
Guixiong, Liu
He, Binyuan
author_facet Huang, Jian
Guixiong, Liu
He, Binyuan
author_sort Huang, Jian
collection PubMed
description Owing to the recent development in deep learning, machine vision has been widely used in intelligent manufacturing equipment in multiple fields, including precision-manufacturing production lines and online product-quality inspection. This study aims at online Machine Vision Inspection, focusing on the method of online semantic segmentation under complex backgrounds. First, the fewer-parameters optimization of the atrous convolution architecture is studied. Atrous spatial pyramid pooling (ASPP) and residual network (ResNet) are selected as the basic architectures of η(seg) and η(main), respectively, which indicate that the improved proportion of the participating input image feature is beneficial for improving the accuracy of feature extraction during the change of the number and dimension of feature maps. Second, this study proposes five modified ResNet residual building blocks, with the main path having a 3 × 3 convolution layer, 2 × 2 skip path, and pooling layer with l(s) = 2, which can improve the use of image features. Finally, the simulation experiments show that our modified structure can significantly decrease segmentation time T(seg) from 719 to 296 ms (decreased by 58.8%), with only a slight decrease in the intersection-over-union from 86.7% to 86.6%. The applicability of the proposed machine vision method was verified through the segmentation recognition of the China Yuan (CNY) for the 2019 version. Compared with the conventional method, the proposed model of semantic segmentation visual detection effectively reduces the detection time while ensuring the detection accuracy and has a significant effect of fewer-parameters optimization. This slows for the possibility of neural network detection on mobile terminals.
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spelling pubmed-78754302021-02-19 Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network Huang, Jian Guixiong, Liu He, Binyuan PLoS One Research Article Owing to the recent development in deep learning, machine vision has been widely used in intelligent manufacturing equipment in multiple fields, including precision-manufacturing production lines and online product-quality inspection. This study aims at online Machine Vision Inspection, focusing on the method of online semantic segmentation under complex backgrounds. First, the fewer-parameters optimization of the atrous convolution architecture is studied. Atrous spatial pyramid pooling (ASPP) and residual network (ResNet) are selected as the basic architectures of η(seg) and η(main), respectively, which indicate that the improved proportion of the participating input image feature is beneficial for improving the accuracy of feature extraction during the change of the number and dimension of feature maps. Second, this study proposes five modified ResNet residual building blocks, with the main path having a 3 × 3 convolution layer, 2 × 2 skip path, and pooling layer with l(s) = 2, which can improve the use of image features. Finally, the simulation experiments show that our modified structure can significantly decrease segmentation time T(seg) from 719 to 296 ms (decreased by 58.8%), with only a slight decrease in the intersection-over-union from 86.7% to 86.6%. The applicability of the proposed machine vision method was verified through the segmentation recognition of the China Yuan (CNY) for the 2019 version. Compared with the conventional method, the proposed model of semantic segmentation visual detection effectively reduces the detection time while ensuring the detection accuracy and has a significant effect of fewer-parameters optimization. This slows for the possibility of neural network detection on mobile terminals. Public Library of Science 2021-02-10 /pmc/articles/PMC7875430/ /pubmed/33566844 http://dx.doi.org/10.1371/journal.pone.0246093 Text en © 2021 Huang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Huang, Jian
Guixiong, Liu
He, Binyuan
Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network
title Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network
title_full Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network
title_fullStr Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network
title_full_unstemmed Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network
title_short Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network
title_sort fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875430/
https://www.ncbi.nlm.nih.gov/pubmed/33566844
http://dx.doi.org/10.1371/journal.pone.0246093
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