Method for Constructing a Façade Dataset through Deep Learning-Based Automatic Image Labeling
Source abstract
Abstract
The construction industry has made great strides in recent decades by utilizing computer programs, including computer aided design programs. However, compared to the manufacturing sector, labor productivity is low because of the high proportion of knowledge-based tasks and simple repetitive tasks. Therefore, knowledge-based task efficiency should be improved through the visual recognition of information by computers. A computer requires a large amount of training data, such as the ImageNet project, to recognize visual information. This paper proposes façade datasets that are efficiently constructed by quickly collecting façade data through road-view images generated from web portals and automatically labeled using deep learning as part of the construction of image datasets for visual recognition construction by a computer. Therefore, we attempted to automatically label façade images to quickly generate large-scale façade datasets with much less effort than the existing research methods. Simultaneously, we constructed datasets for a part of Dongseong-ro, Daegu Metropolitan City, and analyzed their utility and reliability. It was confirmed that the computer could extract significant façade information from the road-view images by recognizing the visual information of the façade image. In addition, we verified the characteristics of the building construction image datasets. This study suggests the possibility of securing quantitative and qualitative façade design knowledge by extracting façade design information from façades anywhere in the world. Previous studies mainly collected façade images through camera photography to construct databases, but in this study, a significant part of the database construction process was shortened through automation. In the case of façade automatic image labeling studies, it is the façade-based automatic 3D modeling which has been primarily studied, but it is difficult to find a study to extract data for façade design research.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · BIM and Construction Integration · 3D Surveying and Cultural Heritage
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Architecture, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea
Funding
Ministry of Land, Infrastructure and Transport · Future Creation Science
References (29)
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