Skip to content

discovery-unicamp/smartparking_unicamp

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

17 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

🚗 Smart Parking System with Deep Learning at Unicamp

This repository contains the complete implementation of a real-time smart parking monitoring system using edge computing and deep learning, representing a decade of research at Unicamp.

The system evolved through multiple research phases, culminating in real-world deployments optimized for accuracy, inference speed, and low-power edge devices.


📝 Feedback: Complaints, Compliments & Suggestions

We value your opinion!
If you have any complaints, compliments, or suggestions about the IC2 Smart Parking Project, please share them with us using the form below:

👉 Fill the Feedback Form

🌎 Português: A sua opinião é muito importante!
Caso tenha reclamações, elogios ou sugestões sobre o Projeto IC2 Smart Parking, compartilhe conosco no formulário abaixo:

👉 Preencha o Formulário de Feedback


📑 Table of Contents


Overview

This system uses deep learning-based object detection to identify free and occupied parking spots in real-time. The design supports deployment on low-power devices such as Raspberry Pi, leveraging TensorFlow Lite optimizations for on-device inference.

It has undergone four major research phases since 2015, progressively improving detection accuracy, inference speed, and robustness under real-world conditions.


Architecture

System Overview


Project Timeline

System Evolution

🚀 2025 – Second Deployment

🔍 2024 – Research Phase 3

🔍 2020–2024 – Research Phase 2

🚀 2019 – First Deployment

🔍 2015–2019 – Research Phase 1

  • Explored GoogleLeNet and Xception for CNN-based parking detection.
  • Presented at PAPIs.io LATAM 2018.

Demonstration

System Demo

🎥 Full demonstration video


Hardware Setup

Full details: 📖 Hardware Documentation

Key components:


Software Implementation

Full details: 📖 Software Documentation

Key modules:


Features

  • Real-time detection of parking spot occupancy.
  • Edge-optimized inference using TensorFlow Lite.
  • Multiple model support (YOLO, EfficientDet, Mask R-CNN).
  • Historical data logging with InfluxDB.
  • Modular hardware and software documentation.

Collaborators

Present:

Past:


How to Cite

@article{da2024smart,
  title={Smart Parking with Pixel-Wise ROI Selection for Vehicle Detection Using YOLOv8, YOLOv9, YOLOv10, and YOLOv11},
  author={da Luz, Gustavo PCP and Sato, Gabriel Massuyoshi and Gonzalez, Luis Fernando Gomez and Borin, Juliana Freitag},
  journal={arXiv preprint arXiv:2412.01983},
  year={2024},
  doi= {10.48550/arXiv.2412.01983}
}

@inproceedings{da202510,
  author = {Gustavo da Luz and Gabriel Sato and Tiago Bannwart and Luis Gonzalez and Juliana Borin},
  title = {10 Years of Deep Learning for Vehicle Detection at a Smart Parking : What has Changed?},
  booktitle = {Anais do IX Workshop de Computação Urbana},
  location = {Natal/RN},
  year = {2025},
  pages = {127--140},
  publisher = {SBC},
  address = {Porto Alegre, RS, Brasil},
  doi = {10.5753/courb.2025.8869},
  url = {https://sol.sbc.org.br/index.php/courb/article/view/35256}
}

License

This project is licensed under the MIT License.

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published