Introduction:

This repo is worked as a computing artifact for Tianjie Zhang's comprehensive exam. The related works have been published in IEEE-T-ITS.

Please cite the paper if you like it.

Data:

The images folder contains all the data utilized in this part. In the images floder, it contains four sub-floders:

Subfolders	Info	Training
cracks	DeepCrack(contains 587 images and their annotation)	300 from DeepCrack
cracks-tradition	traditional augmentation	300 from DeepCrack + 300 from traditional augmentation
cracks-DCGAN	DCGAN	300 from DeepCrack + 300 from DCGAN
cracks-APCGAN	APC-GAN	300 from DeepCrack + 300 from APCGAN

The testing data are all from the DeepCrack which contains 287 crack images.

Models:

Models list (used in the paper):

• AttuNet
• AttuNet-min
• U-Net
• FCN
• LRASPP
• DeepLabv3

Run Order:

First, setup the environment on your PC.

Create the environment in your terminal:

conda env create -f environment.yml 

You can also update an environment to make sure it meets the current requirements in a file:

conda env update -f environment.yml

Second, augment the images.

There are three ways to augment your training dataset in the augmentation folder:

• Random crop (traditional augmentation method)

run random_crop.py : images produced will be stored in augmentation/results/random_crop

• DCGAN

run DCGAN.py : generated real-like images will be stored in augmentation/results/DCGAN

• APCGAN

run APCGAN.py : generated real-like images will be stored in augmentation/results/APCGAN

After get the generated images, annotate these images manually and then put them into the training_images floders to be used for deep-learning training.

Third, training different deep learning methods on the datasets.

1. run the train.py:

1.1 choose and replace a model which you want to run.

'''
 Choose a deep learning method:

    1. net = AttU_Net(img_ch=1, output_ch=1)

    2. net = AttU_Net_min(img_ch=1, output_ch=1)
    
    3. net = UNet(n_channels=1, n_classes=1)

    4. net = fcn_resnet50(num_classes=1)

    5.  net = deeplabv3_resnet50(num_classes= 1)

    6. net = lraspp_mobilenet_v3_large(num_classes=1)
'''

1.2 choose the training dataset

  data_path = "./images/cracks" # todo: your training datasets
  '''
  four choices: 
  "./images/cracks"
  "./images/cracks_tradition"
  "./images/cracks_DCGAN"
  "./images/cracks_APCGAN"
  '''

1.3 you will get a best_model.pth after you run the train.py.

2. Run the test.py

do some modifications when you run the test.py:

 net = AttU_Net(img_ch=1, output_ch=1) # todo: change the model refering to your trained model
 ...

 net.load_state_dict(torch.load('best_model.pth', map_location=device)) # todo
 # todo: make you load your best_model.pth

2.1 you will get the evaluation results of your model in the results folder.

PS: when taining model fcn_resnet50 ,deeplabv3_resnet50 , lraspp_mobilenet_v3_large, remember to modify the last layer, making it consistent with the classes in this work.

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By doing these above, you finished a model setup and evaluation.

Then, you can do the same steps on all the models.

Finally, compare the evalution metrics among models.