singleye / Concrete-Crack-Detection

Crack detection for an autonomous UAV

Concrete-Crack-Detection

This repository contains the code for crack detection in concrete surfaces. It is a TensorFlow implementation of the paper by by Young-Jin Cha and Wooram Choi - "Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks".

The model acheived 85% accuracy on the validation set. A few results are shown below -

MATLAB was used to prepare the data. Regions of Interest were sliced into smaller 128 x 128 pixel images and used for training -

Dependencies required-

• TensorFlow
  
• OpenCV

The dataset.py file creates the training dataset class to be fed into the Convolutional Neural Network. This class automatically determines the number of classes by the number of folders in 'in_dir' (number of folders=number of classes)

NOTE: This script utilizes an already saved cache file. The cache file contains the filenames of the data. If the cache file doesn't exist, a new one created. If the data has been altered with, please delete the old cache file and run the script again. This applies for both Running.py and Train_CD.py. This was my first ever Deep Learning project hence, the naive approach. I'll streamline this once I get some time, or gladly accept a pull request!

The directory structure is assumed to be the following- (For example considering 3 classes)

• in_dir/class1/ - Contains all the training images for class 1
  
  • test/ - Contains all the validation images for class 1
    
• in_dir/class2/ - Contains all the training images for class 2
  
  • test/ - Contains all the validation images for class 2
    
• in_dir/class3/ - Contains all the training images for class 3
  
  • test/ - Contains all the validation images for class 3
    

To train the network run the command with the following arguments:
python Train_CD.py

Argument	Details	Default
--in_dir	path to in_dir folder	cracky
--iter	number of iterations to run the model for	1500
--save_folder	Directory to save checkpoint	CURRENT_DIR

After model has been trained, meta_files are saved into 'save_folder'. To test the model, run the command with the following arguments: python Running.py

Argument	Details	Default
--in_dir	directory containing unlabeled test data	cracky_test
--meta_file	MetaFile path	None (Will throw error if not given)
--CP_dir	dir contatining checkpoint	None (Will throw error if not given)
--save_dir	dir to save output images	CURRENT_DIR

TODO:

• Streamline data loading; remove cache file system
• Combine training and testing scripts into one