Skip to content

Attentional graph neural network for parking slot detection

License

Notifications You must be signed in to change notification settings

khan-yin/gcn-parking-slot

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

23 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Attentional Graph Neural Network for Parking Slot Detection

image

Repository for the paper "Attentional Graph Neural Network for Parking Slot Detection".

@article{gcn-parking-slot:2020,
  title={Attentional Graph Neural Network for Parking Slot Detection},
  author={M. Chen, J. Xu, L. Xiao, D. Zhao etal},
  journal={IEEE Robotics and Automation Letters (RA-L)},
  year={2021},
  volume={6},
  number={2},
  pages={3445-3450},
  doi={10.1109/LRA.2021.3064270}
}

Requirements

  • python 3.6

  • pytorch 1.4+

  • other requirements: pip install -r requirements.txt

Pretrained models

Two pre-trained models can be downloaded with following links.

Link Code Description
Model0 bc0a Trained with ps2.0 subset as in [1]
Model1 pgig Trained with full ps2.0 dataset

Prepare data

The original ps2.0 data and label can be found here. Extract and organize as follows:

├── datasets
│   └── parking_slot
│       ├── annotations
│       ├── ps_json_label 
│       ├── testing
│       └── training

Train & Test

Export current directory to PYTHONPATH:

export PYTHONPATH=`pwd`
  • demo
python3 tools/demo.py -c config/ps_gat.yaml -m cache/ps_gat/100/models/checkpoint_epoch_200.pth
  • train
python3 tools/train.py -c config/ps_gat.yaml
  • test
python3 tools/test.py -c config/ps_gat.yaml -m cache/ps_gat/100/models/checkpoint_epoch_200.pth

References

[1] J. Huang, L. Zhang, Y. Shen, H. Zhang, and Y. Yang, “DMPR-PS: A novel approach for parking-slot detection using directional marking-point regression,” in IEEE International Conference on Multimedia and Expo (ICME), 2019. code

About

Attentional graph neural network for parking slot detection

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 99.8%
  • Shell 0.2%