Detection of tree defects via ground penetrating radar
This report highlights the use of radar based, deep learning driven tree health assessment system. Traditional methods relying on resistograph is deemed insufficient for the challenge, prompting recent research into on using radar as a non-invasive approach to scan and acquire information on the...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/176837 |
| _version_ | 1826117258113449984 |
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| author | Dang, Thanh Nhan |
| author2 | Abdulkadir C. Yucel |
| author_facet | Abdulkadir C. Yucel Dang, Thanh Nhan |
| author_sort | Dang, Thanh Nhan |
| collection | NTU |
| description | This report highlights the use of radar based, deep learning driven tree health assessment
system. Traditional methods relying on resistograph is deemed insufficient for the challenge,
prompting recent research into on using radar as a non-invasive approach to scan and acquire
information on the severity of tree defects. Available technologies can construct a permittivity
map of the target from the radargram, such as migration or inversion algorithms, but at the
expense of computational power. While deep learning driven approaches to permittivity
mapping can reduce the computing time, a considerable dataset is needed. The framework
suggested in this project involves training an encoder decoder convolutional neural network
to perform regression, predicting parameterized defect geometry. The low dimensional
representation of the defect helps in reduce problem’s complexity. Numerical simulations as
well as real measurements suggests the framework’s high accuracy in predicting the position
and extent of defect. |
| first_indexed | 2024-10-01T04:24:40Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/176837 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:24:40Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1768372024-05-24T15:43:27Z Detection of tree defects via ground penetrating radar Dang, Thanh Nhan Abdulkadir C. Yucel Lee Yee Hui School of Electrical and Electronic Engineering NParks acyucel@ntu.edu.sg, EYHLee@ntu.edu.sg Engineering Physics Ground penetrating radar Deep learning Tree This report highlights the use of radar based, deep learning driven tree health assessment system. Traditional methods relying on resistograph is deemed insufficient for the challenge, prompting recent research into on using radar as a non-invasive approach to scan and acquire information on the severity of tree defects. Available technologies can construct a permittivity map of the target from the radargram, such as migration or inversion algorithms, but at the expense of computational power. While deep learning driven approaches to permittivity mapping can reduce the computing time, a considerable dataset is needed. The framework suggested in this project involves training an encoder decoder convolutional neural network to perform regression, predicting parameterized defect geometry. The low dimensional representation of the defect helps in reduce problem’s complexity. Numerical simulations as well as real measurements suggests the framework’s high accuracy in predicting the position and extent of defect. Bachelor's degree 2024-05-20T01:17:56Z 2024-05-20T01:17:56Z 2024 Final Year Project (FYP) Dang, T. N. (2024). Detection of tree defects via ground penetrating radar. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176837 https://hdl.handle.net/10356/176837 en B3001-231 application/pdf Nanyang Technological University |
| spellingShingle | Engineering Physics Ground penetrating radar Deep learning Tree Dang, Thanh Nhan Detection of tree defects via ground penetrating radar |
| title | Detection of tree defects via ground penetrating radar |
| title_full | Detection of tree defects via ground penetrating radar |
| title_fullStr | Detection of tree defects via ground penetrating radar |
| title_full_unstemmed | Detection of tree defects via ground penetrating radar |
| title_short | Detection of tree defects via ground penetrating radar |
| title_sort | detection of tree defects via ground penetrating radar |
| topic | Engineering Physics Ground penetrating radar Deep learning Tree |
| url | https://hdl.handle.net/10356/176837 |
| work_keys_str_mv | AT dangthanhnhan detectionoftreedefectsviagroundpenetratingradar |