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

Seung-Yeul Ji and Han-Jong Jun
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Seung-Yeul Ji: School of Architecture, Hanyang University, Seoul 04763, Korea
Han-Jong Jun: School of Architecture, Hanyang University, Seoul 04763, Korea

Sustainability, 2020, vol. 12, issue 13, 1-18

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.

Keywords: East Asia; traditional buildings; deep learning; artificial intelligence; region-based convolutional neural network (R-CNN); you only look once (YOLO); cloud computing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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