A large-scale fisheye dataset for parking slot detection

The benchmark method will be publicly available upon publication!

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:notebook_with_decorative_cover: Table of Contents

About the Project
- Updates
Getting Started
- Prerequisites
- Installation
Methodology
- Inference
- Training
Results
Contributing
License
Contact
Citation

:star2: About the Project

Autonomous valet parking systems eliminae the need for human drivers to find parking slots, reducing the hassle associated with parking in congested areas. Fisheye imags provise valuable information over a large area instantaneously; nevertheless, no current dataset captures the complexity of parking scenes at the level of granularity required by real-world applications. To address this, we introduce ParkScapes, an fisheye image dataset with highly-accurate, fine-grained annotation for corner-based parking slot labeling. ParkScape provides annotation for 10,000 images, covering a variety of diverse scanarios, including shopping malls, industrial parks, and communities. Please cite if you use it in your work!

:fire: Update

[2024/03/04] We have released the ParkScape, you can download the dataset from here.

:toolbox: Getting Started

:bangbang: Prerequisites

Python 3.8
Pytorch 1.11.0
CUDA 11.3 or higher

:gear: Installation

First, install dependencies

  # clone project 
  git clone https://github.com/Vipermdl/ParkScape

  # install project
  cd ParkScape
  pip install -r requirements.txt

:compass: Benchmark method

:art: Inference

To run the evaluation process, you need to download the model weights

wget -q https://github.com/Vipermdl/releases/download/v0.1.0-alpha/parkscape_detector.pth

Inference with detect.py

python detect.py --weights parkscape_detector.pth --source 0                               # webcam
                                                     img.jpg                         # image
                                                     vid.mp4                         # video
                                                     screen                          # screenshot
                                                     path/                           # directory
                                                     list.txt                        # list of images
                                                     list.streams                    # list of streams
                                                     'path/*.jpg'                    # glob
                                                     'https://youtu.be/LNwODJXcvt4'  # YouTube
                                                     'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

:key: Training

After the model and dataset download automatically, training time for the parking slot detector are 2 days on a NVIDIA 3090 GPU (Multi-GPU times faster). Use the largest --batch-size possible, or pass --batch-size -1 for detector AutoBatch.

python train.py --data parkscape.yaml --epochs 300  --cfg parking_slot_detector.yaml  --batch-size 16

:scroll: Results

Method	Backbone	AP_{50}	AP_{75}	AP	AP_{M}	FPS
CID	HRNet-W32	49.9	46.3	43.9	46.7	15.46
DEKR	HRNet-W32	48.4	45.3	43.3	46.3	16.56
Associative Embedding	HRNet-W32	52.9	43.9	43.8	48.0	5.854
CenterNet	DLA-34	51.4	47.5	44.9	48.5	52.63
Our	CSPDarkNet53	55.1	50.9	47.0	48.1	54.05

:wave: Contributing

Contributions are always welcome!

:warning: License

Distributed under the no License. See LICENSE.txt for more information.

:handshake: Contact

Dongliang Ma - @dongliangma1 - mdl.viper@gmail.com

Project Link: https://github.com/Vipermdl/ParkScape

:gem: Citation

If ParkScape is useful or relevant to your research, please kindly recognize our contributions by citing our paper:

@ARTICLE{fu2024parkscape,
  author={Fu, Li and Ma, Dongliang and Qu, Xin and Jiang, Xin and Shan, Lie and Zeng, Dan},
  journal={IEEE Transactions on Instrumentation and Measurement}, 
  title={ParkScape: A Large-Scale Fisheye Dataset for Parking Slot Detection and a Benchmark Method}, 
  year={2024},
  volume={73},
  number={},
  pages={1-13},
  keywords={Cameras;Distortion;Autonomous vehicles;Detectors;Convolution;Lighting;Annotations;Autonomous driving;cameras;datasets;fisheye images;parking slot detection},
  doi={10.1109/TIM.2024.3406840}}

Vipermdl / ParkScape

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