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|:ok_hand:[link]|:ok_hand:[link](psw: 3fai)|Fine-tuned| |RoadNet|:ok_hand:[link]|:ok_hand:[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 >>>]

PS: Once you downloaded the pretrained models, you can follow the test example in the jupyter notebook to test your own images.

• 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).