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Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm
Wood is widely used in construction, the home, and art applications all over the world because of its good mechanical properties and aesthetic value. However, because the growth and preservation of wood are greatly affected by the environment, it often contains different types of defects that affect...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570981/ https://www.ncbi.nlm.nih.gov/pubmed/32957519 http://dx.doi.org/10.3390/s20185315 |
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author | Ding, Fenglong Zhuang, Zilong Liu, Ying Jiang, Dong Yan, Xiaoan Wang, Zhengguang |
author_facet | Ding, Fenglong Zhuang, Zilong Liu, Ying Jiang, Dong Yan, Xiaoan Wang, Zhengguang |
author_sort | Ding, Fenglong |
collection | PubMed |
description | Wood is widely used in construction, the home, and art applications all over the world because of its good mechanical properties and aesthetic value. However, because the growth and preservation of wood are greatly affected by the environment, it often contains different types of defects that affect its performance and ornamental value. To solve the issues of high labor costs and low efficiency in the detection of wood defects, we used machine vision and deep learning methods in this work. A color charge-coupled device camera was used to collect the surface images of two types of wood from Akagi and Pinus sylvestris trees. A total of 500 images with a size of 200 × 200 pixels containing wood knots, dead knots, and checking defects were obtained. The transfer learning method was used to apply the single-shot multibox detector (SSD), a target detection algorithm and the DenseNet network was introduced to improve the algorithm. The mean average precision for detecting the three types of defects, live knots, dead knots and checking was 96.1%. |
format | Online Article Text |
id | pubmed-7570981 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75709812020-10-28 Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm Ding, Fenglong Zhuang, Zilong Liu, Ying Jiang, Dong Yan, Xiaoan Wang, Zhengguang Sensors (Basel) Article Wood is widely used in construction, the home, and art applications all over the world because of its good mechanical properties and aesthetic value. However, because the growth and preservation of wood are greatly affected by the environment, it often contains different types of defects that affect its performance and ornamental value. To solve the issues of high labor costs and low efficiency in the detection of wood defects, we used machine vision and deep learning methods in this work. A color charge-coupled device camera was used to collect the surface images of two types of wood from Akagi and Pinus sylvestris trees. A total of 500 images with a size of 200 × 200 pixels containing wood knots, dead knots, and checking defects were obtained. The transfer learning method was used to apply the single-shot multibox detector (SSD), a target detection algorithm and the DenseNet network was introduced to improve the algorithm. The mean average precision for detecting the three types of defects, live knots, dead knots and checking was 96.1%. MDPI 2020-09-17 /pmc/articles/PMC7570981/ /pubmed/32957519 http://dx.doi.org/10.3390/s20185315 Text en © 2020 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 Ding, Fenglong Zhuang, Zilong Liu, Ying Jiang, Dong Yan, Xiaoan Wang, Zhengguang Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm |
title | Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm |
title_full | Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm |
title_fullStr | Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm |
title_full_unstemmed | Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm |
title_short | Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm |
title_sort | detecting defects on solid wood panels based on an improved ssd algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570981/ https://www.ncbi.nlm.nih.gov/pubmed/32957519 http://dx.doi.org/10.3390/s20185315 |
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