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Innovative Multidisciplinary Methodology for the Analysis of Traditional Marginal Architecture
Open AccessArticle

Deep Learning Model for Form Recognition and Structural Member Classification of East Asian Traditional Buildings

School of Architecture, Hanyang University, Seoul 04763, Korea
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Sustainability 2020, 12(13), 5292; https://doi.org/10.3390/su12135292
Received: 17 May 2020 / Revised: 17 June 2020 / Accepted: 22 June 2020 / Published: 30 June 2020
(This article belongs to the Special Issue The Exploration of Sustainability in Traditional Rural Buildings)
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. View Full-Text
Keywords: East Asia; traditional buildings; deep learning; artificial intelligence; region-based convolutional neural network (R-CNN); you only look once (YOLO); cloud computing East Asia; traditional buildings; deep learning; artificial intelligence; region-based convolutional neural network (R-CNN); you only look once (YOLO); cloud computing
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Ji, S.-Y.; Jun, H.-J. Deep Learning Model for Form Recognition and Structural Member Classification of East Asian Traditional Buildings. Sustainability 2020, 12, 5292.

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