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

Martinez Rodriguez, Pablo, Barkokebas, Beda, Hamzeh, Farook, Al-Hussein, Mohamed and Ahmad, Rafiq (2021) A vision-based approach for automatic progress tracking of floor paneling in offsite construction facilities. Automation in Construction, 125. p. 103620. ISSN 0926-5805

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Official URL: https://doi.org/10.1016/j.autcon.2021.103620


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.

Item Type: Article
Additional Information: Funding information: The authors gratefully acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (File No. IRCPJ 419145-15).
Uncontrolled Keywords: Offsite construction, Construction automation, Computer vision, Productivity, Machine learning, Task efficiency
Subjects: H300 Mechanical Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Rachel Branson
Date Deposited: 09 Dec 2021 11:15
Last Modified: 17 Feb 2022 03:31
URI: http://nrl.northumbria.ac.uk/id/eprint/47941

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