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

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 datadriven 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 360degree 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.