YOLOv5-whorlDetector

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YOLOv5-whorlDetector

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This repo contains the R scripts to detect whorls from dense drone laser scanning point clouds.

YOLOv5-whorlDetector 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# YOLOv5-whorlDetector
This repo contains the R scripts to detect whorls from dense drone laser scanning point clouds.

The full methodology is described in:

Puliti, S., McLean, J.P., Cattaneo, N., Fischer, C., Astrup, R. 2022. Tree height-growth trajectory estimation using uni-temporal UAV laser scanning data and deep learning. _Forestry_ volume .... pp .... DOI: .....

![schematic_overview_methods_gituhb](https://user-images.githubusercontent.com/5663984/164417744-bb5badfd-d8e8-41dc-9431-df448951e234.png)

# Usage
In this repo you will be able to process a single tree according to the methodology developed by Puliti et al. 2022. The code is mainly based on R but calls python for the YOLO inference. Thus python should be installed on the machine.

## 1- First clone the repository
```shell
git clone https://github.com/stefp/YOLOv5-whorlDetector
```

## 2 - Download the whorl detector model (*.pt) the google drive
The model can be dowloaded from this link (https://drive.google.com/file/d/1_kNcQrUuSiYxvjItw_nBJWiRq9YIdR0D/view?usp=sharing) and should be stored in the .../YOLOv5-whorlDetector/src/whorl_detector_weights folder

## 3 - run the "Use.r" file

YOLOv5-whorlDetector

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