A vision-based approach for automatic progress tracking of floor paneling in offsite construction facilities

Offsite construction is an approach focused on moving construction tasks from traditional jobsites to manufacturing facilities. Improved productivity of construction tasks is paramount in terms of competitiveness and is achieved through the continuous improvement of operations and planning, which of...

Full description

Saved in:
Bibliographic Details
Published in:Automation in construction 2021-05, Vol.125, p.103620, Article 103620
Main Authors: Martinez, Pablo, Barkokebas, Beda, Hamzeh, Farook, Al-Hussein, Mohamed, Ahmad, Rafiq
Format: Article
Language:English
Subjects:
Algorithms
Cameras
Closed circuit television
Computer vision
Construction automation
Continuous improvement
Finite state machines
Floors
Machine learning
Manufacturing
Offsite construction
Productivity
Security management
Task efficiency
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Offsite construction is an approach focused on moving construction tasks from traditional jobsites to manufacturing facilities. Improved productivity of construction tasks is paramount in terms of competitiveness and is achieved through the continuous improvement of operations and planning, which often relies on historical data obtained from previous projects. Despite being a common practice, current methods, such as time studies, are not able to capture the changing scenarios resulting from improvements to production. This paper presents a novel approach to automatically detect and track the progress of construction operations by applying a method that combines deep learning algorithms and finite state machines to existing footage captured by closed-circuit television (CCTV) security cameras. Applied in the context of floor panel manufacturing stations, the proposed method examines entire production days recorded by CCTV cameras, while providing the durations of each task, its required resources, and the task efficiency per panel with high accuracy. •A novel framework to automatically detect and track progress of offsite construction operations.•A deep learning and finite state machines hybrid method is applied to CCTV footage of floor paneling workstations.•Task durations, utilization of resources, and production efficiency per panel are estimated with high accuracy.
ISSN:0926-5805
1872-7891
DOI:10.1016/j.autcon.2021.103620