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

Author

Listed:
  • Seung-Yeul Ji

    (School of Architecture, Hanyang University, Seoul 04763, Korea)

  • Han-Jong Jun

    (School of Architecture, Hanyang University, Seoul 04763, Korea)

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.

Suggested Citation

  • Seung-Yeul Ji & Han-Jong Jun, 2020. "Deep Learning Model for Form Recognition and Structural Member Classification of East Asian Traditional Buildings," Sustainability, MDPI, vol. 12(13), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:13:p:5292-:d:378510
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    Citations

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    Cited by:

    1. Donghwa Shon & Giyoung Byun & Soyoung Choi, 2023. "Identification of Facade Elements of Traditional Areas in Seoul, South Korea," Land, MDPI, vol. 12(2), pages 1-22, January.
    2. Hail Jung & Jeongjin Rhee, 2022. "Application of YOLO and ResNet in Heat Staking Process Inspection," Sustainability, MDPI, vol. 14(23), pages 1-14, November.

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