gcn-parking-slot
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Attentional graph neural network for parking slot detection
Attentional Graph Neural Network for Parking Slot Detection
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