<!-- Hey, thanks for using the awesome-readme-template template. If you have any enhancements, then fork this project and create a pull request or just open an issue with the label "enhancement". Don't forget to give this project a star for additional support ;) Maybe you can mention me or this repo in the acknowledgements too --> <div align="center"> <!-- <img src="assets/logo.png" alt="logo" width="200" height="auto" /> --> <h1>A large-scale fisheye dataset for parking slot detection</h1> <p> The benchmark method will be publicly available upon publication! </p> <!-- Badges --> <p> <a href="https://github.com/Vipermdl/ParkScape/graphs/contributors"> <img src="https://img.shields.io/github/contributors/Vipermdl/ParkScape" alt="contributors" /> </a> <a href=""> <img src="https://img.shields.io/github/last-commit/Vipermdl/ParkScape" alt="last update" /> </a> <a href="https://github.com/Vipermdl/ParkScape/network/members"> <img src="https://img.shields.io/github/forks/Vipermdl/ParkScape" alt="forks" /> </a> <a href="https://github.com/Vipermdl/ParkScape/stargazers"> <img src="https://img.shields.io/github/stars/Vipermdl/ParkScape" alt="stars" /> </a> <a href="https://github.com/Vipermdl/ParkScape/issues/"> <img src="https://img.shields.io/github/issues/Vipermdl/ParkScape" alt="open issues" /> </a> <!-- <a href="https://github.com/Vipermdl/ParkScape/blob/master/LICENSE"> <img src="https://img.shields.io/github/license/Vipermdl/ParkScape.svg" alt="license" /> </a> --> </p> <h4> <a href="https://github.com/Vipermdl/ParkScape">View Demo</a> <span> · </span> <a href="https://github.com/Vipermdl/ParkScape">Documentation</a> <span> · </span> <a href="https://github.com/Vipermdl/ParkScape/issues/">Report Bug</a> <span> · </span> <a href="https://github.com/Vipermdl/ParkScape/issues/">Request Feature</a> </h4> </div> <br /> <!-- Table of Contents -->

:notebook_with_decorative_cover: Table of Contents

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

<!-- About the Project -->

:star2: About the Project

<div style="color:#0000FF" align="center"> <img src="imgs/fig1.png"/> </div>

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.

<!-- Getting Started -->

:toolbox: Getting Started

<!-- Prerequisites -->

:bangbang: Prerequisites

• Python 3.8
• Pytorch 1.11.0
• CUDA 11.3 or higher

<!-- Installation -->

:gear: Installation

First, install dependencies

  # clone project 
  git clone https://github.com/Vipermdl/ParkScape
  
  # install project
  cd ParkScape
  pip install -r requirements.txt

<!-- Roadmap -->

: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                                                              

<!-- Code of Conduct -->

: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

<a href="https://github.com/Vipermdl/ParkScape/graphs/contributors"> <img src="https://contrib.rocks/image?repo=Vipermdl/ParkScape" /> </a>

Contributions are always welcome!

<!-- FAQ --> <!-- ## :grey_question: FAQ - Question 1 + Answer 1 - Question 2 + Answer 2 --> <!-- License -->

:warning: License

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

<!-- Contact -->

:handshake: Contact

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

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

<!-- Acknowledgments -->

: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}}