A deep learning-based approach to facilitate the as-built state recognition of indoor construction works

Ekanayake, B; Ahmadian Fard Fini, A; Wong, JKW; Smith, P

A deep learning-based approach to facilitate the as-built state recognition of indoor construction works

Ekanayake, B

Ahmadian Fard Fini, A Wong, JKW Smith, P

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Publisher:: Emerald
Publication Type:: Journal Article
Citation:: Construction Innovation, 2022
Issue Date:: 2022-12-20

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Field	Value	Language
dc.contributor.author	Ekanayake, B https://orcid.org/0000-0002-9929-1586
dc.contributor.author	Ahmadian Fard Fini, A
dc.contributor.author	Wong, JKW
dc.contributor.author	Smith, P
dc.date.accessioned	2023-01-16T05:11:14Z
dc.date.available	2023-01-16T05:11:14Z
dc.date.issued	2022-12-20
dc.identifier.citation	Construction Innovation, 2022
dc.identifier.issn	1471-4175
dc.identifier.issn	1477-0857
dc.identifier.uri	http://hdl.handle.net/10453/165023
dc.description.abstract	Purpose: Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works. Design/methodology/approach: The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images. Findings: The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images. Originality/value: This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.
dc.language	en
dc.publisher	Emerald
dc.relation.ispartof	Construction Innovation
dc.relation.isbasedon	10.1108/CI-05-2022-0121
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1202 Building, 1503 Business and Management, 1504 Commercial Services
dc.title	A deep learning-based approach to facilitate the as-built state recognition of indoor construction works
dc.type	Journal Article
utslib.for	1202 Building
utslib.for	1503 Business and Management
utslib.for	1504 Commercial Services
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2023-01-16T05:11:13Z
pubs.publication-status	Published

Abstract:

Purpose: Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works. Design/methodology/approach: The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images. Findings: The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images. Originality/value: This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

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http://hdl.handle.net/10453/165023