Method for Constructing a Façade Dataset through Deep Learning-Based Automatic Image Labeling
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. T...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/15/7570 |
| _version_ | 1797414676560084992 |
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| author | Hyeongmo Gu Seungyeon Choo |
| author_facet | Hyeongmo Gu Seungyeon Choo |
| author_sort | Hyeongmo Gu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T05:37:01Z |
| format | Article |
| id | doaj.art-ae6778be40a944b1843f6c1bb988c8c4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:37:01Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ae6778be40a944b1843f6c1bb988c8c42023-12-03T12:28:03ZengMDPI AGApplied Sciences2076-34172022-07-011215757010.3390/app12157570Method for Constructing a Façade Dataset through Deep Learning-Based Automatic Image LabelingHyeongmo Gu0Seungyeon Choo1School of Architecture, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, KoreaSchool of Architecture, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, KoreaThe 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.https://www.mdpi.com/2076-3417/12/15/7570façadeexterior building informationdeep learningimage processingimage identificationimage extraction |
| spellingShingle | Hyeongmo Gu Seungyeon Choo Method for Constructing a Façade Dataset through Deep Learning-Based Automatic Image Labeling Applied Sciences façade exterior building information deep learning image processing image identification image extraction |
| title | Method for Constructing a Façade Dataset through Deep Learning-Based Automatic Image Labeling |
| title_full | Method for Constructing a Façade Dataset through Deep Learning-Based Automatic Image Labeling |
| title_fullStr | Method for Constructing a Façade Dataset through Deep Learning-Based Automatic Image Labeling |
| title_full_unstemmed | Method for Constructing a Façade Dataset through Deep Learning-Based Automatic Image Labeling |
| title_short | Method for Constructing a Façade Dataset through Deep Learning-Based Automatic Image Labeling |
| title_sort | method for constructing a facade dataset through deep learning based automatic image labeling |
| topic | façade exterior building information deep learning image processing image identification image extraction |
| url | https://www.mdpi.com/2076-3417/12/15/7570 |
| work_keys_str_mv | AT hyeongmogu methodforconstructingafacadedatasetthroughdeeplearningbasedautomaticimagelabeling AT seungyeonchoo methodforconstructingafacadedatasetthroughdeeplearningbasedautomaticimagelabeling |