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This repository focuses on the automation of blade inspection, using different Computer Vision (CV) approaches and methods to detect damage on the wind turbine blades.

Python 99.86% Shell 0.14%

damage_detection_maskrcnn's Introduction

Mask R-CNN for damage detection on Windmill Blade

Requirements

Python 3.4, TensorFlow 1.3, Keras 2.0.8 and other common packages listed in requirements.txt.

Installation

Clone this repository

$ git clone http://141.252.12.43/adions025/maskrcnn.git

Install dependencies

  pip install -r requirements.txt

Run setup from the repository root directory

   python setup.py install

How to train in this project - Training Process

you can find the class in src/main/damageDetection.py, allows to training your own dataset

$ python damageDetection.py train --dataset=/home/student_5/workspace/Mask_RCNN/dataset/ --weights=imagenet --logs=/home/student_5/workspace/Mask_RCNN/logs/

if your annotations are made by labelimg you need to use this:

convert your annotations xml to json for diferences regions:

you can also find this file inside of dataset folder, just run this file ConverterXMLtoJson.py [moreinfo]
```
$ python converterXMLtoJSON.py
```

before run put your images .jpg and your .xml file inside /train and /val
you need to have this structure :
/Mask_rcnn
- /dataset
  - /train
  - /val
  - converterXMLtoJson.py

Versioning

You can use binary segmentation version or multiclass, just use version.sh file
Make sure that this file has the necessary execution permissions.
```
chmod +x versions.sh
```
You will get two folders with the different code versions in the previous path.

Additional information:

Mask R-CNN needs eggs, run you setup.py file to generate again.
If you have installation problems, you can use the same enviroment (enviroment.yml) conda.
```
$ conda env create -f environment.yml
```
You can now activate the enviroment
```
$ cconda activate myenv
```
You can find more info about how to manage conda enviroments Creating an enviroment from an enviroment.yml file [moreinfo]