corrosion condition state classification for bridge inspections

The four semantic classes in the dataset are:

Good (Background)
Fair
Poor
Severe

Coming soon in January (?) 2022 🟢[Paper] 🟢[Dataset] 🟢[Trained models]

The corrosion condition state segmentation dataset which can be used for the localization of structural damage, and for more futuristic style transfer SPADE and GAN / GAN-Inversion applications.

We were able to achieve an f1-score of 86.67% using the weighted cross entropy classes model. This included using augmented data.

The most important environment configurations are the following:

• Pytorch >= 1.4
• Python >= 3.6
• tqdm
• matplotlib
• sklearn
• cv2
• Pillow
• pandas
• shutil

• Clone the respository.
• Download the DeeplabV3+ 🟢trained model weights
• Configure run_metrics_evaluation.py

You will get the f1 score, the jaccard index, and the confusion matrix. We suggest running this in an IDE.

Once training has converged or when it has stopped, we can used the best checkpoint based on the validation data results. This checkpoint is loaded and our test data is evaluated.

• Clone the respository.

run_show_results__.py

• gets predicted masks
• gets combined mask and image overaly
• gets one-hot-encoded vector images of predictions

1. Clone the repository
2. Download the 🟢dataset
3. Go into the Training folder
4. Create a DATA folder
5. Copy and paste the Train and Test folders for 512x512 images from the dataset you downloaded into the DATA folder
6. The DATA folder should have a folder called 'Train' and a folder called 'Test'. Inside each of those folders include the mask and image pairs in their respective folders (Masks, Images).
7. If you have set this up correctly then you are now ready to begin.

Neccesary and optional inputs to the main_plus.py file: ('-' means it is neccessary, '--' means that these are optional inputs)

 -data_directory = dataset directory path (expects there to be a 'Test' and a 'Train' folder, with folders 'Masks' and 'Images')
 -exp_directory = where the stored metrics and checkpoint weights will be stored
 --epochs = number of epochs
 --batchsize = batch size
 --output_stride = deeplab hyperparameter for output stride
 --channels = number of classes (we have four, the default has been set to four). 
 --class_weights = weights for the cross entropy loss function
 --folder_structure = 'sep' or 'single' (sep = separate (Test, Train), single = only looks at one folder (Train). If you want to get validation results instead of getting back your test dataset results then you should use 'single'. If you want to test directly on the Test dataset then you should use 'sep'.
 --pretrained = if there is a pretrained model to start with then include the path to the model weights here. 

Run the following command:

python main_plus.py -data_directory '/PATH TO DATA DIRECTORY/' -exp_directory '/PATH TO SAVE CHECKPOINTS/' \
--epochs 40 --batch 2

During training there are model checkpoints saved every epoch. At these checkpoints the model is compared against the test or validation data. If the test or validation scores are better than the best score, then it is saved.

1. Clone the repository
2. Ensure your image and mask data is 512x512 pixels. (can use the rescale_image.py in Pre-processing)
3. Ensure that if you resized your masks to 512x512 that they did not interpolate the colors into more color classes than you have. The expected format is BGR. (can use the rescale_segmentation.py in Pre-processing)
4. You now need to go into the datahandler_plus.py file and edit the colors as necessary. For example, the Structural Materials dataset used the following format, which is in the datahandler_plus.py in this repository.

# color mapping corresponding to classes
# ---------------------------------------------------------------------
# 0 = Good (Black)
# 1 = Fair (Red)
# 2 = Poor (Green)
# 3 = Severe (Yellow)
# ---------------------------------------------------------------------
self.mapping = {(0,0,0): 0, (0,0,128): 1, (0,128,0): 2, (0,128,128): 3}

6. Adjust the number of 'channels' in the training command to match the number of channels that you have.
7. Ensure that your DATA folder has a folder called 'Train' and a folder called 'Test'. Inside each of those folders include the mask and image pairs in their respective folders (Masks, Images).
8. If you have set this up correctly then you are now ready to begin.

(The images in the dataset were annotated using labelme. We suggest that you use this tool)

0. If you are planning to extend on the corrosion dataset, then please read the annotation guidelines provided by the author in the 🟢 corrosion dataset repository.

1. Before beginning to annotate, we suggest that you use jpeg for the RGB image files. We advised against beginning with images which are already resized.

2. We have put together a tutorial on tips and tricks on how to use the labelme software in this youtube video. We also made a video on youtube showing how to set up labelme with Anaconda prompt.

3. After annotating you will have matching JSON and jpeg files, indicating the annotation and image pair respectfully.

4. You will take these files and generate masks and one-hot-encoded vector files using run_labelme2voc_.py file in Pre-processing. Then you can re-scale these images and masks using the respective files in Pre-processing. You can also use the random sort function we have created to randomly split the data.

The labels_corrosion_segmentation.txt file contains the class labels needed for the run_labelme2voc_.py function. If your classes are different then they need to be reflected in this particular file.

Corrosion Condition State Dataset:

Bianchi, Eric; Hebdon, Matthew (2021): Corrosion Condition State Semantic Segmentation Dataset. 
University Libraries, Virginia Tech. Dataset. https://doi.org/10.7294/16624663.v2

Corrosion Condition State Model:

Bianchi, Eric; Hebdon, Matthew (2021): Trained Model for the Semantic Segmentation of Structural Material. 
University Libraries, Virginia Tech. Software. https://doi.org/10.7294/16628620.v1 

Paper:

@article{doi:10.1061/(ASCE)CP.1943-5487.0001045,
author = {Eric Bianchi  and Matthew Hebdon },
title = {Development of Extendable Open-Source Structural Inspection Datasets},
journal = {Journal of Computing in Civil Engineering},
volume = {36},
number = {6},
pages = {04022039},
year = {2022},
doi = {10.1061/(ASCE)CP.1943-5487.0001045},