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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/22/10935 |
| _version_ | 1797511300908056576 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:43:25Z |
| format | Article |
| id | doaj.art-a256c1ff6e1a407c96416c248af5340e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:43:25Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a256c1ff6e1a407c96416c248af5340e2023-11-22T22:21:09ZengMDPI AGApplied Sciences2076-34172021-11-0111221093510.3390/app112210935Tree Internal Defected Imaging Using Model-Driven Deep Learning NetworkHongju Zhou0Liping Sun1Hongwei Zhou2Man Zhao3Xinpei Yuan4Jicheng Li5College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaThe 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.https://www.mdpi.com/2076-3417/11/22/10935n/a |
| spellingShingle | Hongju Zhou Liping Sun Hongwei Zhou Man Zhao Xinpei Yuan Jicheng Li Tree Internal Defected Imaging Using Model-Driven Deep Learning Network Applied Sciences n/a |
| title | 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_short | Tree Internal Defected Imaging Using Model-Driven Deep Learning Network |
| title_sort | tree internal defected imaging using model driven deep learning network |
| topic | n/a |
| url | https://www.mdpi.com/2076-3417/11/22/10935 |
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