Source code and replication data for camera-based smart parking vehicle detection using deep learning models on edge devices

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Luz, Gustavo Pessoa Caixeta Pinto da; Borin, Juliana Freitag; Gomez Gonzalez, Luis Fernando, 2026, "Source code and replication data for camera-based smart parking vehicle detection using deep learning models on edge devices", https://doi.org/10.25824/redu/Q3WUTU, Repositório de Dados de Pesquisa da Unicamp, V1

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Description	This dataset corresponds to the research developed in the MSc dissertation "Selection and Optimization of Pre-Trained Deep Learning Models for Camera-Based Smart Parking Systems on Edge Devices". We share the source code, instructions to reproduce the experiments, and instructions to build the smart parking system on GitHub. The full image dataset cannot be made available on REDU due to privacy considerations, as the images were captured in public university spaces and may contain identifiable individuals and vehicles. The dataset is stored with restricted access at the Institute of Computing, Unicamp, in line with a conservative approach to potential privacy issues. For more information or access requests, please contact the point of contact of the deposit. (2026-05-28)
Subject	Computer and Information Science
Keyword	Smart parking, Deep learning, Edge computing, Internet of things (IoT), Computer vision
Related Publication	Gustavo Pessoa Caixeta Pinto da Luz. Selection and Optimization of Pre-Trained Deep Learning Models for Camera-Based Smart Parking Systems on Edge Devices. MSc Dissertation, Institute of Computing, University of Campinas (Unicamp), 2026. Supervisor: Profa. Dra. Juliana Freitag Borin. Co-supervisor: Dr. Luis Fernando Gomez Gonzalez.
Notes	Research developed within the Discovery Laboratory, Institute of Computing, Unicamp, in collaboration with the Smart Campus Unicamp initiative.
License/Data Use Agreement	CC BY-NC 4.0


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Citation Metadata

Persistent Identifier	doi:10.25824/redu/Q3WUTU
Publication Date	2026-06-03
Title	Source code and replication data for camera-based smart parking vehicle detection using deep learning models on edge devices
Alternative URL	https://github.com/discovery-unicamp/smartparking_unicamp/releases/tag/v2.0-beta
Author	Luz, Gustavo Pessoa Caixeta Pinto da (Universidade Estadual de Campinas (UNICAMP). Instituto de Computação) - ORCID: https://orcid.org/0000-0001-9496-5791 Borin, Juliana Freitag (Universidade Estadual de Campinas (UNICAMP). Instituto de Computação) - ORCID: https://orcid.org/0000-0002-8603-699X Gomez Gonzalez, Luis Fernando (Universidade Estadual de Campinas (UNICAMP). Instituto de Computação) https://orcid.org/0000-0001-7131-8989
Point of Contact	Use email button above to contact. Borin, Juliana Freitag (Universidade Estadual de Campinas (UNICAMP). Instituto de Computação)
Description	This dataset corresponds to the research developed in the MSc dissertation "Selection and Optimization of Pre-Trained Deep Learning Models for Camera-Based Smart Parking Systems on Edge Devices". We share the source code, instructions to reproduce the experiments, and instructions to build the smart parking system on GitHub. The full image dataset cannot be made available on REDU due to privacy considerations, as the images were captured in public university spaces and may contain identifiable individuals and vehicles. The dataset is stored with restricted access at the Institute of Computing, Unicamp, in line with a conservative approach to potential privacy issues. For more information or access requests, please contact the point of contact of the deposit. (2026-05-28)
Subject	Computer and Information Science
Keyword	Smart parking Deep learning (Ei (Compendex)) Edge computing (DLC (LCSH)) Internet of things (IoT) (DLC (LCSH)) Computer vision (DLC (LCSH))
Related Publication	Gustavo Pessoa Caixeta Pinto da Luz. Selection and Optimization of Pre-Trained Deep Learning Models for Camera-Based Smart Parking Systems on Edge Devices. MSc Dissertation, Institute of Computing, University of Campinas (Unicamp), 2026. Supervisor: Profa. Dra. Juliana Freitag Borin. Co-supervisor: Dr. Luis Fernando Gomez Gonzalez. G. P. C. P. da Luz, G. M. Sato, L. F. G. Gonzalez, J. F. Borin. Smart parking with pixel-wise ROI selection for vehicle detection using YOLOv8, YOLOv9, YOLOv10, and YOLOv11. Internet of Things, vol. 36, p. 101858, 2026. doi: 10.1016/j.iot.2025.101858 https://doi.org/10.1016/j.iot.2025.101858 G. P. C. P. da Luz, A. M. A. Narvaez, T. G. Bannwart, G. M. Sato, L. F. G. Gonzalez, J. F. Borin. Spot-Wise Smart Parking: An Edge-Enabled Architecture with YOLOv11 Towards a Digital Twin. Journal of Internet Services and Applications, vol . 17(1), 110-120, 2026. doi: 10.5753/jisa.2026.6664 https://doi.org/10.5753/jisa.2026.6664 G. M. Sato, G. P. C. P. da Luz, L. F. G. Gonzalez, J. F. Borin. People Counting Application with Crowded Scenarios: A Case Study with TV Boxes as Edge Devices. Journal of Internet Services and Applications, vol. 16, no. 1, pp. 470–479, 2025. doi: 10.5753/jisa.2025.5156 https://doi.org/10.5753/jisa.2025.5156 G. P. C. P. da Luz, G. M. Sato, T. G. Bannwart, L. F. G. Gonzalez, J. F. Borin. 10 Years of Deep Learning for Vehicle Detection at a Smart Parking: What Has Changed? Workshop de Computação Urbana (CoUrb), SBC, pp. 127–140, 2025. doi: 10.5753/courb.2025.8869 https://doi.org/10.5753/courb.2025.8869 T. G. Bannwart, G. P. C. P. da Luz, G. M. Sato, L. F. G. Gonzalez, J. F. Borin. TV Boxes as Support Servers for IoT Applications. I Workshop on Sustainable Computing and Technology Reuse (SCORE), SBC, pp. 1–4, 2025. doi: 10.5753/score.2025.17175 https://doi.org/10.5753/score.2025.17175
Notes	Research developed within the Discovery Laboratory, Institute of Computing, Unicamp, in collaboration with the Smart Campus Unicamp initiative.
Contributor	Brazilian Ministry of Science, Technology and Innovations, with resources from Law num. 8,248, of October 23, 1991, within the scope of PPI-SOFTEX,coordinated by Softex and published Arquitetura Cognitiva (Phase 3): DOU 01245.003479/2024-10
Funding Information	Coordenação de Aperfeiçoamento de Pessoal de Nível Superior: CAPES: 88887.999360/2024-00 Fundação de Amparo à Pesquisa do Estado de São Paulo: FAPESP: 2023/00811-0
Depositor	Pessoa Caixeta Pinto da Luz, Gustavo
Deposit Date	2026-05-28
Declarações obrigatórias sobre ética e privacidade	o projeto que gerou os dados foi aprovado pelo Comite de Ética em Pesquisa da Unicamp ou não envolve questões que requeiram tal aprovação; os dados que serão depositados estão de acordo com a LGPD (Lei Geral de Proteção de Dados)

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