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Utilizing 360-Degree Images for Synthetic Data Generation in Construction Scenarios

  • Aqsa Sabir
  • Rahat Hussain
  • Syed Farhan Alam Zaidi
  • Akeem Pedro
  • Mehrtash Soltani
  • Dongmin Lee
  • Chansik Park

Computer vision-based safety monitoring requires machine learning models trained on generalized datasets covering various viewpoints, surface properties, and lighting conditions. However, capturing high-quality and extensive datasets for some construction scenarios is challenging at real job sites due to the risky nature of construction scenarios. Previous methods have proposed synthetic data generation techniques involving 2D background randomization with virtual objects in game-based engines. While there has been extensive work on utilizing 360-degree images for various purposes, no study has yet employed 360-degree images for generating synthetic data specifically tailored for construction sites. To improve the synthetic data generation process, this study proposes a 360-degree images-based synthetic data generation approach using Unity 3D game engine. The approach efficiently generates a sizable dataset with better dimensions and scaling, encompassing a range of camera positions with randomized lighting intensities. To check the effectiveness of our proposed method, we conducted a subjective evaluation, considering three key factors: object positioning, scaling in terms of object respective size, and the overall size of the generated dataset. The synthesized images illustrate the visual improvement in all three factors. By offering an improved data generation method for training safety-focused computer vision models, this research has the potential to significantly enhance the automation of the construction safety monitoring process, and hence, this method can bring substantial benefits to the construction industry by improving operational efficiency and reinforcing safety measures for workers

  • Keywords:
  • 360-Degree Images,
  • Computer Vision,
  • Synthetic Data Generation,
  • Game Engine,
  • Object Detection,
  • Construction Safety Monitoring,
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Aqsa Sabir

Chung Ang University, Korea (the Republic of) - ORCID: 0009-0006-5459-909X

Rahat Hussain

Chung Ang University, Korea (the Republic of) - ORCID: 0000-0002-6909-5189

Syed Farhan Alam Zaidi

Chung Ang University, Korea (the Republic of) - ORCID: 0000-0003-2257-290X

Akeem Pedro

Chung Ang University, Korea (the Republic of) - ORCID: 0000-0002-7884-5316

Mehrtash Soltani

Chung Ang University, Korea (the Republic of) - ORCID: 0000-0002-5217-2010

Dongmin Lee

Chung Ang University, Korea (the Republic of) - ORCID: 0000-0002-3176-5327

Chansik Park

Chung Ang University, Korea (the Republic of) - ORCID: 0000-0003-2256-300X

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  • Publication Year: 2023
  • Pages: 701-710

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  • Publication Year: 2023

Chapter Information

Chapter Title

Utilizing 360-Degree Images for Synthetic Data Generation in Construction Scenarios

Authors

Aqsa Sabir, Rahat Hussain, Syed Farhan Alam Zaidi, Akeem Pedro, Mehrtash Soltani, Dongmin Lee, Chansik Park

DOI

10.36253/979-12-215-0289-3.70

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

35

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