Pixel-level Crack Detection From Images Of Levee Systems : A Comparative Study

PIXEL-LEVEL CRACK DETECTION FROM IMAGES OF LEVEE SYSTEMS : A COMPARATIVE STUDY

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

You would need to install the following software before replicating this framework in your local or server machine.

Python version 3.7+
Aanaconda version 3+
TensorFlow version 2.6.0
Keras version 2.6.0

Download and install code

• Retrieve the code

git clone https://github.com/manisa/IGARSS2022_LeveeCrackDetection.git
cd IGARSS2022_LeveeCrackDetection

• Create and activate the virtual environment with python dependendencies.

conda create -n gpu-tf tensorflow-gpu
conda activate gpu-tf
source installPackages.sh

Download datasets

• Go to this link.
• Click on LeveeCrack_dataset.zip. This will automatically download the datasets used to to perform 10FCV.
• Unzip and copy all the datasets from LeveeCrack_dataset directory into the folder LeveeCrack_dataset inside the root folder IGARSS2022_LeveeCrackDetection.
• Your directory structure should look like this:

IGARSS2022_LeveeCrackDetection/
    LeveeCrack_dataset/
        images/
        masks/

Folder Structure

IGARSS2022_LeveeCrackDetection/
    archs/
    lib/
    src/
    LeveeCrack_dataset/

Training

• To replicate the training procedure, follow following command line.

cd src
python 10FCV_train_multiresunet.py

Authors

Manisha Panta, Md Tamjidul Hoque, Mahdi Abdelguerfi, Maik C. Flanagin

License

This project is licensed under the MIT License - see the LICENSE.md file for details