context-based-parking-slot-detect

Tensorflow implementation of Context-based parking slot detection (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
• Unzip to $your_data_path/train folder

2. Download Test Dataset

• link
• 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
• 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
• 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 dataset to fit our format.
• link

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}}
'''