Deep learning algorithm for tree defect characterization
This study explores the potential of using Deep Learning (DL) to develop a non-invasive method for assessing the health of trees, with a focus on characterizing defects in tree trunks and estimating their size using Ground Penetrating Radar (GPR) images. The aim is to reduce the number of treefall i...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2023
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/167679 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This study explores the potential of using Deep Learning (DL) to develop a non-invasive method for assessing the health of trees, with a focus on characterizing defects in tree trunks and estimating their size using Ground Penetrating Radar (GPR) images. The aim is to reduce the number of treefall incidents and improve tree management, especially in cities like Singapore, which have very high tree population. A successful DL-based model could help arborists assess trees quickly and accurately, improving the efficiency of tree health assessment and reducing the risk of accidents and fatalities. |
|---|