https://github.com/dohoseok/context-based-parking-slot-detect

https://github.com/dohoseok/context-based-parking-slot-detect

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/dohoseok/context-based-parking-slot-detect
• Owner: dohoseok
• Created: 2020-09-14T03:45:46.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2021-12-28T01:16:08.000Z (almost 3 years ago)
• Last Synced: 2024-04-09T23:30:53.951Z (9 months ago)
• Language: Python
• Size: 85.9 KB
• Stars: 87
• Watchers: 4
• Forks: 27
• Open Issues: 14
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-parking-slot-detection - Context-Based Parking Slot Detection With a Realistic Dataset

README

        
# context-based-parking-slot-detect

Tensorflow implementation of [Context-based parking slot detection](https://ieeexplore.ieee.org/abstract/document/9199853) (IEEE Access)

This implementation is based on https://github.com/wizyoung/YOLOv3_TensorFlow

# Prepare Dataset (PIL-park)

0. This code should be run only once at the beginning.

1. Download Train Dataset

 - [link](https://drive.google.com/file/d/1i6I-71g1fNL7_Qh-Qs1oOKLclrP2qUmO/view?usp=sharing)

 - Unzip to $your_data_path/train folder

2. Download Test Dataset

 - [link](https://drive.google.com/file/d/1z94Oqcy0Dich1GgiMkyPY5-wltsL8_hq/view?usp=sharing)

 - Unzip to $your_data_path/test folder

 

3. Data augmentation, create tfrecord and text files

 - python prepare_data.py --data_path=$your_data_path

# Train Dataset

1. Download pretrain weight (Updated 2020.10.26)

 - [link](https://drive.google.com/drive/folders/1mXbNgNxyXPi7JNsnBaxEv1-nWr7SVoQt)

 - Save to 'pre_weight' folder under "context-based detect" folder

 

2. python train.py --data_path=$your_data_path

3. Trained Weight path

- Weight files of parking context recognizer are saved to 'weight_pcr/YYYYMMDD_HHMM'

- Weight files of parking slot detector fine-tuned for parallel parking slots are saved to 'weight_psd/type_0/YYYYMMDD_HHMM'

- Weight files of parking slot detector fine-tuned for perpendicular parking slots are saved to 'weight_psd/type_1/YYYYMMDD_HHMM'

- Weight files of parking slot detector fine-tuned for diagonal parking slots are saved to 'weight_psd/type_2/YYYYMMDD_HHMM'

# Test Method (with downloaded weight files)

1. Download trained weight

 - [link](https://drive.google.com/file/d/1g3PXkTn8-pmIotjJqX_aR1ZPJNrrmWKG/view?usp=sharing)

 - Unzip under main path (locate "weight_pcr" and "weight_psd" under "context-based detect" folder)

 

2. Evaluate

 - python test.py --data_path=$your_test_path

 

# Test Method (with your trained weight files)

1. Evaluate

 - python test.py --data_path=$your_test_path --pcr_test_weight='weight_pcr/YYYYMMDD_HHMM/cp-0050.ckpt' --psd_test_weight_type0='weight_psd/type_0/YYYYMMDD_HHMM' --psd_test_weight_type1='weight_psd/type_1/YYYYMMDD_HHMM' --psd_test_weight_type2='weight_psd/type_2/YYYYMMDD_HHMM'

 

 

 

# Converted dataset of ps2.0

- Converted version of the [ps2.0](https://cslinzhang.github.io/deepps/) dataset to fit our format.

- [link](https://drive.google.com/file/d/1vM_u_YNFTdv7eHhwn4ExXE98_7BpSa3X/view?usp=sharing)

 

 

 # Citation

If you use this code for your research, please cite the following work:

``` 

 @ARTICLE{9199853,

  author={Do, Hoseok and Choi, Jin Young},

  journal={IEEE Access}, 

  title={Context-Based Parking Slot Detection With a Realistic Dataset}, 

  year={2020},

  volume={8},

  number={},

  pages={171551-171559},

  doi={10.1109/ACCESS.2020.3024668}}

'''