Tree Internal Defected Imaging Using Model-Driven Deep Learning Network

The health of trees has become an important issue in forestry. How to detect the health of trees quickly and accurately has become a key area of research for scholars in the world. In this paper, a living tree internal defect detection model is established and analyzed using model-driven theory, whe...

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Autores principales:	Hongju Zhou, Liping Sun, Hongwei Zhou, Man Zhao, Xinpei Yuan, Jicheng Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	n/a Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/a256c1ff6e1a407c96416c248af5340e
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id	oai:doaj.org-article:a256c1ff6e1a407c96416c248af5340e
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spelling	oai:doaj.org-article:a256c1ff6e1a407c96416c248af5340e2021-11-25T16:41:22ZTree Internal Defected Imaging Using Model-Driven Deep Learning Network10.3390/app1122109352076-3417https://doaj.org/article/a256c1ff6e1a407c96416c248af5340e2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10935https://doaj.org/toc/2076-3417The health of trees has become an important issue in forestry. How to detect the health of trees quickly and accurately has become a key area of research for scholars in the world. In this paper, a living tree internal defect detection model is established and analyzed using model-driven theory, where the theoretical fundamentals and implementations of the algorithm are clarified. The location information of the defects inside the trees is obtained by setting a relative permittivity matrix. The data-driven inversion algorithm is realized using a model-driven algorithm that is used to optimize the deep convolutional neural network, which combines the advantages of model-driven algorithms and data-driven algorithms. The results of the comparison inversion algorithms, the BP neural network inversion algorithm, and the model-driven deep learning network inversion algorithm, are analyzed through simulations. The results shown that the model-driven deep learning network inversion algorithm maintains a detection accuracy of more than 90% for single defects or homogeneous double defects, while it can still have a detection accuracy of 78.3% for heterogeneous multiple defects. In the simulations, the single defect detection time of the model-driven deep learning network inversion algorithm is kept within 0.1 s. Additionally, the proposed method overcomes the high nonlinearity and ill-posedness electromagnetic inverse scattering and reduces the time cost and computational complexity of detecting internal defects in trees. The results show that resolution and accuracy are improved in the inversion image for detecting the internal defects of trees.Hongju ZhouLiping SunHongwei ZhouMan ZhaoXinpei YuanJicheng LiMDPI AGarticlen/aTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10935, p 10935 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	n/a Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	n/a Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Hongju Zhou Liping Sun Hongwei Zhou Man Zhao Xinpei Yuan Jicheng Li Tree Internal Defected Imaging Using Model-Driven Deep Learning Network
description	The health of trees has become an important issue in forestry. How to detect the health of trees quickly and accurately has become a key area of research for scholars in the world. In this paper, a living tree internal defect detection model is established and analyzed using model-driven theory, where the theoretical fundamentals and implementations of the algorithm are clarified. The location information of the defects inside the trees is obtained by setting a relative permittivity matrix. The data-driven inversion algorithm is realized using a model-driven algorithm that is used to optimize the deep convolutional neural network, which combines the advantages of model-driven algorithms and data-driven algorithms. The results of the comparison inversion algorithms, the BP neural network inversion algorithm, and the model-driven deep learning network inversion algorithm, are analyzed through simulations. The results shown that the model-driven deep learning network inversion algorithm maintains a detection accuracy of more than 90% for single defects or homogeneous double defects, while it can still have a detection accuracy of 78.3% for heterogeneous multiple defects. In the simulations, the single defect detection time of the model-driven deep learning network inversion algorithm is kept within 0.1 s. Additionally, the proposed method overcomes the high nonlinearity and ill-posedness electromagnetic inverse scattering and reduces the time cost and computational complexity of detecting internal defects in trees. The results show that resolution and accuracy are improved in the inversion image for detecting the internal defects of trees.
format	article
author	Hongju Zhou Liping Sun Hongwei Zhou Man Zhao Xinpei Yuan Jicheng Li
author_facet	Hongju Zhou Liping Sun Hongwei Zhou Man Zhao Xinpei Yuan Jicheng Li
author_sort	Hongju Zhou
title	Tree Internal Defected Imaging Using Model-Driven Deep Learning Network
title_short	Tree Internal Defected Imaging Using Model-Driven Deep Learning Network
title_full	Tree Internal Defected Imaging Using Model-Driven Deep Learning Network
title_fullStr	Tree Internal Defected Imaging Using Model-Driven Deep Learning Network
title_full_unstemmed	Tree Internal Defected Imaging Using Model-Driven Deep Learning Network
title_sort	tree internal defected imaging using model-driven deep learning network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a256c1ff6e1a407c96416c248af5340e
work_keys_str_mv	AT hongjuzhou treeinternaldefectedimagingusingmodeldrivendeeplearningnetwork AT lipingsun treeinternaldefectedimagingusingmodeldrivendeeplearningnetwork AT hongweizhou treeinternaldefectedimagingusingmodeldrivendeeplearningnetwork AT manzhao treeinternaldefectedimagingusingmodeldrivendeeplearningnetwork AT xinpeiyuan treeinternaldefectedimagingusingmodeldrivendeeplearningnetwork AT jichengli treeinternaldefectedimagingusingmodeldrivendeeplearningnetwork
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Tree Internal Defected Imaging Using Model-Driven Deep Learning Network

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