DeepSegmentor

A Pytorch implementation of DeepCrack and RoadNet projects.

1.Datasets

• Crack Detection Dataset
• Multi-task Road Detection Dataset

Please download the corresponding dataset and prepare it by following the guidance.

2.Installation

We provide an user-friendly configuring method via Conda system, and you can create a new Conda environment using the command:

conda env create -f environment.yml

3.Balancing Weights

We follow the Median Frequency Balancing method, using the command:

python3 ./tools/calculate_weights.py --data_path <path_to_segmentation>

4.Training

Before the training, please download the dataset and copy it into the folder datasets.

• Crack Detection

sh ./scripts/train_deepcrack.sh <gpu_id>

• Road Detection

sh ./scripts/train_roadnet.sh <gpu_id>

We provide our pretrained models here:

Model	Google Drive	Baidu Yun	Others
DeepCrack	👌[link]	👌[link](psw: 3fai)	Fine-tuned
RoadNet	👌[link]	👌[link](psw: c2gi)	Roughly trained
RoadNet++	[link]	[link]	-

5.Testing

• Crack Detection

sh ./scripts/test_deepcrack.sh <gpu_id>

Image	Ground Truth	GF	fused	side1	side2	side3	side4	side5

[See more examples >>>]

• Road Detection

sh ./scripts/test_roadnet.sh <gpu_id>

Image	Ground Truth	Prediction

[See more examples >>>]

6.Evaluation

• Metrics (appeared in our papers):

Metric	Description	Usage
P	Precision, TP/(TP+FP)	segmentation
R	Recall, TP/(TP+FN)	segmentation
F	F-score, 2PR/(P+R)	segmentation
TPR	True Positive Rate, TP/(TP+FN)	segmentation
FPR	False Positive Rate, FP/(FP+TN)	segmentation
AUC	The Area Under the ROC Curve	segmentation
G	Global accuracy, measures the percentage of the pixels correctly predicted	segmentation
C	Class average accuracy, means the predictive accuracy over all classes	segmentation
I/U	Mean intersection over union	segmentation
ODS	the best F-measure on the dataset for a fixed scale	edge,centerline
OIS	the aggregate F-measure on the dataset for the best scale in each image	edge,centerline
AP	the average precision on the full recall range	edge,centerline

Note: If you want to apply the standard non-maximum suppression (NMS) for edge/centerline thinning. Please see more details in Piotr's Structured Forest matlab toolbox or some helper functions provided in the hed/eval.

[See more details (Evaluation + Guided Filter + CRF) >>>]

Usage:

cd eval
python eval.py --metric_mode prf --model_name deepcrack --output deepcrack.prf

[Display the accuracy curves >>>]

Acknowledgment

• This code is based on the pytorch-CycleGAN-and-pix2pix. Thanks to the contributors of this project.

• If you are familar to Google Colab, there is an implementation of Colab version (provided by DZDL/DeepSegmentor). Besides, there is a WebApp version of this project: crack-detector.

References

If you take use of our datasets or code, please cite our papers:

@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}
}

@article{liu2019roadnet,
  title={RoadNet: Learning to Comprehensively Analyze Road Networks in Complex Urban Scenes from High-Resolution Remotely Sensed Images},
  author={Liu, Yahui and Yao, Jian and Lu, Xiaohu and Xia, Menghan and Wang, Xingbo and Liu, Yuan},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={57},
  number={4},
  pages={2043--2056},
  year={2019},
  doi={10.1109/TGRS.2018.2870871}
}

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