Development of a refined illumination and reflectance approach for optimal construction site interior image enhancement

Wong, KW; Mojtaba, M; Alireza, AFF; Mohammad, AAG; Mahdi, A; Michael, E

Development of a refined illumination and reflectance approach for optimal construction site interior image enhancement

Wong, KW Mojtaba, M Alireza, AFF Mohammad, AAG Mahdi, A Michael, E

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Publisher:: Emerald
Publication Type:: Journal Article
Citation:: Construction Innovation: information, process, management, 2022
Issue Date:: 2022-09-08

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Field	Value	Language
dc.contributor.author	Wong, KW
dc.contributor.author	Mojtaba, M
dc.contributor.author	Alireza, AFF
dc.contributor.author	Mohammad, AAG
dc.contributor.author	Mahdi, A
dc.contributor.author	Michael, E
dc.date.accessioned	2022-09-15T04:54:07Z
dc.date.available	2022-09-15T04:54:07Z
dc.date.issued	2022-09-08
dc.identifier.citation	Construction Innovation: information, process, management, 2022
dc.identifier.issn	1471-4175
dc.identifier.issn	1471-4175
dc.identifier.uri	http://hdl.handle.net/10453/161834
dc.description.abstract	Purpose – Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-ofthe- art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy. Design/methodology/approach – To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress. Findings – The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance. Originality/value – The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.
dc.language	en
dc.publisher	Emerald
dc.relation	A W Edwards
dc.relation.ispartof	Construction Innovation: information, process, management
dc.relation.isbasedon	10.1108/CI-02-2022-0044
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1202 Building, 1503 Business and Management, 1504 Commercial Services
dc.title	Development of a refined illumination and reflectance approach for optimal construction site interior image enhancement
dc.type	Journal Article
utslib.location.activity	Australia
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	2022-09-15T04:53:56Z
pubs.publication-status	Published online

Abstract:

Purpose – Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-ofthe- art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy. Design/methodology/approach – To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress. Findings – The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance. Originality/value – The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

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http://hdl.handle.net/10453/161834