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DeepCrack: A Deep Hierarchical Feature Learning Architecture for Crack Segmentation, Neurocomputing.

crack-detection image-segmentation dataset crack-segmentation deep-learning deep-neural-networks

deepcrack's Introduction

DeepCrack: A Deep Hierarchical Feature Learning Architecture for Crack Segmentation

Resources: [paper] | [code] | [dataset]
Architecture: based on Holistically-Nested Edge Detection, ICCV 2015, [Paper][code].

Dataset:

We established a public benchmark dataset with cracks in multiple scales and scenes to evaluate the crack detection systems. All of the crack images in our dataset are manually annotated.

Please note that we own the copyrights to part of original crack images and all annotated maps. Their use is RESTRICTED to non-commercial research and educational purposes.

You can find the dataset in ./dataset, and here are the details:

Folder	Description
`train_img`	RGB images for training
`train_lab`	binary annotation for training images
`test_img`	RGB images for testing
`test_lab`	binary annotation for testing images

A brief overview on our crack detection dataset:

Reference:

If you use this dataset for your research, please cite our paper:

@article{liu2019deepcrack,
  title={DeepCrack: A Deep Hierarchical Feature Learning Architecture for Crack Segmentation},
  author={Liu, Yahui and Yao, Jian and Lu, Xiaohu and Xie, Renping and Li, Li},
  journal={Neurocomputing},
  volume={338},
  pages={139--153},
  year={2019},
  doi={10.1016/j.neucom.2019.01.036}
}

If you have any questions, please contact me: yahui.cvrs AT gmail.com without hesitation.

deepcrack's People

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deepcrack's Issues

ROC Curve

Hello Sir!
Can you please provide code to generate the ROC curve, Figure 8 in the paper?

RuntimeError

Hello, @yhlleo
I am facing this problem is it possible to get any clarification on what I should do?

RuntimeError: Error(s) in loading state_dict for DeepCrackNet:
Unexpected key(s) in state_dict: "conv1.1.weight", "conv1.1.bias", "conv1.4.weight", "conv1.4.bias", "conv2.1.weight", "conv2.1.bias", "conv2.4.weight", "conv2.4.bias", "conv3.1.weight", "conv3.1.bias", "conv3.4.weight", "conv3.4.bias", "conv3.7.weight", "conv3.7.bias", "conv4.1.weight", "conv4.1.bias", "conv4.4.weight", "conv4.4.bias", "conv4.7.weight", "conv4.7.bias", "conv5.1.weight", "conv5.1.bias", "conv5.4.weight", "conv5.4.bias", "conv5.7.weight", "conv5.7.bias".
size mismatch for side_conv1.weight: copying a param with shape torch.Size([1, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([2, 64, 1, 1]).
size mismatch for side_conv2.weight: copying a param with shape torch.Size([1, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([2, 128, 1, 1]).
size mismatch for side_conv3.weight: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([2, 256, 1, 1]).
size mismatch for side_conv4.weight: copying a param with shape torch.Size([1, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([2, 512, 1, 1]).
size mismatch for side_conv5.weight: copying a param with shape torch.Size([1, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([2, 512, 1, 1]).
size mismatch for fuse_conv.weight: copying a param with shape torch.Size([1, 5, 1, 1]) from checkpoint, the shape in current model is torch.Size([2, 10, 1, 1]).

Process finished with exit code 1

Data annotation

Hi, I'd like to know whether the cracks are annotated as lines (e.g polyline in ESRI ArcGIS) or polygons. It seems that some thin cracks are unsuitable to be annotated as polygons.
thanks a lot!

Dataset annotation

Hi , I'd like to know which tool you used for annotation ?

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