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Related Experiment Video

Updated: Jun 20, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

A multimodal dataset for environmental occupancy detection.

Guilherme Dall'Agnol Deconto1, Avelino Francisco Zorzo1, Roben Castagna Lunardi1,2

  • 1Pontifical Catholic University of Rio Grande do Sul, Av. Ipiranga, 6681, Partenon, Porto Alegre/RS CEP 90619-900, Brazil.

Data in Brief
|June 19, 2026
PubMed
Summary

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A new multimodal dataset from a smart environment in Brazil offers valuable data for occupancy detection and energy usage analysis in Internet of Things (IoT) deployments. This resource supports smart building research and development.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Environmental Science

Background:

  • Smart environments require comprehensive data for accurate analysis.
  • Existing datasets may not capture the complexity of real-world heterogeneous Internet of Things (IoT) deployments.
  • Multimodal data collection is crucial for understanding dynamic environmental and usage patterns.

Purpose of the Study:

  • To present a novel multimodal dataset from a real smart environment.
  • To provide a rich resource for research in occupancy detection, energy usage, and anomaly detection.
  • To document a replicable smart building deployment for future studies.

Main Methods:

  • Collected environmental, electrical, and device-interaction data from a heterogeneous IoT deployment.
Keywords:
ESP32Environmental monitoringInternet of Things (IoT)Multimodal sensor dataOccupancy detectionReal-world dataset

Related Experiment Videos

Last Updated: Jun 20, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

  • Utilized commercial smart devices and a custom ESP32-based sensing node.
  • Implemented a two-stage annotation procedure for human-verified occupancy ground truth using YOLO26m and manual review.

    • Main Results:

    • The dataset includes continuous measurements of environmental variables (CO2, temperature, humidity, light, sound), electrical parameters (power, voltage, current), and device states.
    • Human-verified occupancy data was generated through an automated and manual review process.
    • Data were collected under natural operating conditions, reflecting real-world fluctuations and network challenges.

    Conclusions:

    • The presented dataset offers a valuable and publicly available resource for advancing research in smart environments.
    • The documented deployment serves as a model for future smart building experiments and comparative studies.
    • This multimodal dataset can significantly contribute to the development of intelligent building management systems.