Built on python, this repository contains the code for crack detection in concrete surfaces. A generic image classification program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception.
This model has been pre-trained for the ImageNet Large Visual Recognition Challenge using the data from 2012, and it can differentiate between 1,000 different classes, like Dalmatian, dishwasher etc. The program applies Transfer Learning to this existing model and re-trains it to classify a new set of images.
Here in this project, it has been retrained to differentiate between cracked and non-cracked surfaces.
Make sure you have Python installed, then install Tensorflow on your system, and clone this repo. 'pip install tensorflow==1.4'
A folder named dataset has already been created which contains two sub-folders namely 'Crack' and 'No Crack'. Download the dataset from this link
This enables classification of images between the data sets.
Go to the project directory and run:
$ bash run.sh
This script installs the Inception model and initiates the re-training process for the specified image data sets.
Once the process is complete, it will return a training accuracy somewhere between 85% - 100%.
The training summaries, trained graphs and trained labels will be saved in a folder named logs.
python classify.py test.jpg
Where test.jpg is the input file which is to be classified.
The classifier will output the predictions for each data set. A prediction score between 0.8 to 1 is considered to be optimal.
