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논문 기본 정보

자료유형
학술저널
저자정보
김진성 (한양대학교) 송재열 (한양대학교) 김하얀 (한양대학교) 이진국 (한양대학교)
저널정보
한국퍼실리티매니지먼트학회 한국퍼실리티매니지먼트학회지 한국퍼실리티매니지먼트학회지 제12권 제2호
발행연도
2017.12
수록면
73 - 80 (8page)

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This paper aims to propose an approach to auto-recognition of office objects using non-professionally taken indoor pictures based on the deep learning technique. Recently, artificial intelligence(AI) has been applied in broad fields of industry. Especially, the deep learning-based image recognition and object detection technologies are attaining a high level of accuracy close to human capability. In addition, its source technology has been open to the public, allowing people to use it according to their own purpose. According to the AI trend, deep-learning also has been actively studied in the field of building technology. This paper describes an approach to utilizing image recognition at the phase of facility management (FM) and the process for verifying the possibility of adapting up-to-date technologies in FM fields. The procedure of this study includes data collection, data preprocessing, deep learning-based model training and office indoor image auto-recognition test for detecting office objects. The target office objects include office desks, office chairs and electronic devices, which are most commonly seen in the office space. Over 200 indoor images including target office objects are collected from the domestic office furniture firms' catalog for model training and testing. The results of test with new indoor image are analyzed as the factor of accuracy and similarity of detected office objects. This paper also depicts the potential to apply the auto-recognition technique with BIM(Building Information Modeling) for supporting CAFM (Computer-Aided Facility Management).

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