Journal of the Architectural Institute of Korea Planning & Design (대한건축학회논문집:계획계)

Architectural Institute of Korea (대한건축학회)
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A Development of Façade Dataset Construction Technology Using Deep Learning-based Automatic Image Labeling

딥러닝 기반 이미지 자동 레이블링을 활용한 건축물 파사드 데이터세트 구축 기술 개발

  • Received : 2019.10.01
  • Accepted : 2019.12.18
  • Published : 2019.12.27
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Abstract

The construction industry has made great strides in the past decades by utilizing computer programs including CAD. However, compared to other manufacturing sectors, labor productivity is low due to the high proportion of workers' knowledge-based task in addition to simple repetitive task. Therefore, the knowledge-based task efficiency of workers should be improved by recognizing the visual information of computers. A computer needs a lot of training data, such as the ImageNet project, to recognize visual information. This study, aim at proposing building facade datasets that is efficiently constructed by quickly collecting building facade data through portal site road view and automatically labeling using deep learning as part of construction of image dataset for visual recognition construction by the computer. As a method proposed in this study, we constructed a dataset for a part of Dongseong-ro, Daegu Metropolitan City and analyzed the utility and reliability of the dataset. Through this, it was confirmed that the computer could extract the significant facade information of the portal site road view by recognizing the visual information of the building facade image. Additionally, In contribution to verifying the feasibility of building construction image datasets. this study suggests the possibility of securing quantitative and qualitative facade design knowledge by extracting the facade design knowledge from any facade all over the world.

Keywords

Acknowledgement

Supported by : 한국연구재단

이 연구는 2019년도 정부(미래창조과학부)의 재원으로 한국연구재단의 지원을 받아 수행된 이공분야기초연구사업임. 과제번호 : 2019R1A2C2006983

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