Deep Learning Model for Form Recognition and Structural Member Classification of East Asian Traditional Buildingsopen access
- Authors
- Ji, Seung-Yeul; Jun, Han-Jong
- Issue Date
- Jul-2020
- Publisher
- MDPI
- Keywords
- East Asia; traditional buildings; deep learning; artificial intelligence; region-based convolutional neural network (R-CNN); you only look once (YOLO); cloud computing
- Citation
- SUSTAINABILITY, v.12, no.13, pp.1 - 19
- Indexed
- SCIE
SSCI
SCOPUS
- Journal Title
- SUSTAINABILITY
- Volume
- 12
- Number
- 13
- Start Page
- 1
- End Page
- 19
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145430
- DOI
- 10.3390/su12135292
- Abstract
- The unique characteristics of traditional buildings can provide fresh insights for sustainable building development. In this study, a deep learning model and methodology were developed for classifying traditional buildings by using artificial intelligence (AI)-based image analysis technology. The model was constructed based on expert knowledge of East Asian buildings. Videos and images from Korea, Japan, and China were used to determine building types and classify and locate structural members. Two deep learning algorithms were applied to object recognition: a region-based convolutional neural network (R-CNN) to distinguish traditional buildings by country and you only look once (YOLO) to recognise structural members. A cloud environment was used to develop a practical model that can handle various environments in real time.
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Collections - 서울 공과대학 > 서울 건축학부 > 1. Journal Articles
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