/Damage_Detection_MaskRCNN

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.

Primary LanguagePython

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

  1. Clone this repository
$ git clone http://141.252.12.43/adions025/maskrcnn.git
  1. Install dependencies
  pip install -r requirements.txt
  1. 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]

Authors