Game Engine-Based Synthetic Dataset Generation of Entities on Construction Site

Shenghan Li, Yaolin Zhang and Yi Tan ()
Additional contact information
Shenghan Li: Shenzhen University
Yaolin Zhang: The Shenzhen University
Yi Tan: The Shenzhen University

A chapter in Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate, 2023, pp 1602-1614 from Springer

Abstract: Abstract Computer vision has been widely used in construction sites for progress monitoring, and safety monitoring. However, collecting data from construction sites and labeling them into datasets is a time-consuming, labor-intensive, and costly task. Therefore, a synthetic dataset generation approach for construction site entities based on the game engine is proposed to solve the problem of the lack of construction site datasets. In this research, construction site scene models are formulated by grouping existing digital on-site assets, and image annotation and camera calibration files are automatically generated by developed scripts in the selected game engine. The movement of the model is also controlled by developed scripts and the scene is rendered using High-Definition Rendering Pipeline (HDRP) to obtain high-resolution images. Components such as transform and Box Collider are used to get the coordinates of the object relative to the camera and the size of the bounding box, and to automatically generate the labels. In addition, the focal length, field of view (FOV), and other parameters of the camera component are utilized to calculate the camera Intrinsic when generating calibration files. By this method, a large amount of synthetic data can be quickly acquired and labeled, significantly reducing the time of dataset generation of on-site entities. Finally, the computer vision model trained on the synthetic dataset achieved 91.6% mAP on the real dataset.

Keywords: Computer vision; Construction site; Game engine; Synthetic Dataset (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-99-3626-7_123

Ordering information: This item can be ordered from
http://www.springer.com/9789819936267

DOI: 10.1007/978-981-99-3626-7_123

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().