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EXPORT DATE: 2 June 2025

@CONFERENCE{Toledo2025,
author={Toledo, Mauricio J. and Lorca, Macarena and Mora, Miguel },
editor={Seppänen, Olli  and Koskela, Lauri  and Murata , Koichi  },
title={Use of Computer Vision and Object Detection to Measure Construction Productivity Using Crew Balance Charts}, 
journal={Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33)}, 
booktitle={Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33)}, 
year={2025},
pages={810-821},
url={http://www.iglc.net/papers/details/2360},
doi={10.24928/2025/0184},
affiliation={Assistant Professor, Head of Civil Engineering Department, Universidad Andres Bello, Chile, mauricio.toledo@unab.cl, orcid.org/0000-0002-3903-7260 ; Civil Engineer, Universidad Andres Bello, Chile, m.lorcamadriaza@uandresbello.edu ; Associated Professional, Instituto Profesional IACC, Santiago, Chile, mimora@ing.uchile.cl, orcid.org/0009-0003-4285-8670 },
abstract={Experts in the construction industry identify artificial intelligence (AI) technologies as a key strategy for improving productivity. In Chile, construction productivity has stagnated over the past two decades. This study explores the use of computer vision and a machine learning (ML) algorithm to measure productivity reliably, aiming to improve processes and support data-driven decision-making.
This research uses the YOLOv5 algorithm to detect workers' body postures from video and image data. Body postures are categorized as Productive or Contributory Work based on a predefined taxonomy. The algorithm was trained using 1,500 images extracted from 74 360-degree videos captured using a GoPro camera, representing over five hours of slab formwork installation. Experimental results achieved a mean average precision (mAP 0.5) exceeding 85%.
For productivity measurement, fixed-camera recordings captured images at five-second intervals. YOLOv5 detected postures for key tasks, including: installing perimeter taping (IPT), installing plumbed props (IPP), installing supporting beams (ISB), and installing formwork panels (IFP). Results were visualized through Crew Balance Charts, comparing YOLOv5-based and manually constructed analyses. IFP exhibited the best performance results and most of detected images corresponded to Productive Work. },
author_keywords={Computer vision, productivity, crew balance chart, object detection, AI. },
address={Osaka and Kyoto, Japan },
issn={2789-0015 },
publisher={ },
language={English},
document_type={Conference Paper},
source={IGLC},
}