33rd Annual Conference of the International Group for Lean Construction (IGLC 33) - Osaka and Kyoto, Japan - 2025

https://doi.org/10.24928/2025/0184

Use of Computer Vision and Object Detection to Measure Construction Productivity Using Crew Balance Charts

Mauricio J. Toledo1, Macarena Lorca2 & Miguel Mora3

1Assistant Professor, Head of Civil Engineering Department, Universidad Andres Bello, Chile, [email protected], orcid.org/0000-0002-3903-7260
2Civil Engineer, Universidad Andres Bello, Chile, [email protected]
3Associated Professional, Instituto Profesional IACC, Santiago, Chile, [email protected], 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.

Keywords

Computer vision, productivity, crew balance chart, object detection, AI.

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Reference in APA 7th edition format:

Toledo, M. J., Lorca, M. & Mora, M.. (2025). Use of Computer Vision and Object Detection to Measure Construction Productivity Using Crew Balance Charts. In Seppänen, O., Koskela, L., & Murata , K. (Eds.), Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33) (pp. 810–821). https://doi.org/10.24928/2025/0184

Shortened reference for use in IGLC papers:

Toledo, M. J., Lorca, M. & Mora, M.. (2025). Use of Computer Vision and Object Detection to Measure Construction Productivity Using Crew Balance Charts. IGLC33. https://doi.org/10.24928/2025/0184

 

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