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Intelligent Recognition Method of Decorative Openwork Windows with Sustainable Application for Suzhou Traditional Private Gardens in China

Author

Listed:
  • Rui Zhang

    (School of Landscape Architecture, Zhejiang A & F University, Hangzhou 311300, China)

  • Yuwei Zhao

    (School of Landscape Architecture, Zhejiang A & F University, Hangzhou 311300, China)

  • Jianlei Kong

    (School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
    National Engineering Laboratory for Agri-Product Quality Traceability, Beijing 100048, China)

  • Chen Cheng

    (School of Landscape Architecture, Zhejiang A & F University, Hangzhou 311300, China)

  • Xinyan Liu

    (School of Landscape Architecture, Zhejiang A & F University, Hangzhou 311300, China)

  • Chang Zhang

    (School of Landscape Architecture, Zhejiang A & F University, Hangzhou 311300, China)

Abstract

Decorative openwork windows (DO-Ws) in Suzhou traditional private gardens play a vital role in Chinese traditional garden art. Due to the delicate and elegant patterns, as well as their rich cultural meaning, DO-Ws have quite high protection and utilization value. In this study, we firstly visited 15 extant traditional gardens in Suzhou and took almost 3000 photos to establish the DO-W datasets. Then, we present an effective visual recognition method named CSV-Net to classify different DO-Ws’ patterns in Suzhou traditional gardens. On the basis of the backbone module of the cross stage partial network optimized with the Soft-VLAD architecture, the proposed CSV-Net achieves a preferable representation ability for distinguishing different DO-Ws in practical scenes. The comparative experimental results show that the CSV-Net model achieves a good balance between its performance, robustness and complexity for identifying DO-Ws, also having further potential for sustainable application in traditional gardens. Moreover, the Canglang Pavilion and the Humble Administrator’s Garden were selected as the cases to analyze the relation between identifying DO-W types and their locations in intelligent approaches, which further reveals the design rules of the sustainable culture contained in Chinese traditional gardens. This work ultimately promotes the sustainable application of artificial intelligence technology in the field of garden design and inheritance of the garden art.

Suggested Citation

  • Rui Zhang & Yuwei Zhao & Jianlei Kong & Chen Cheng & Xinyan Liu & Chang Zhang, 2021. "Intelligent Recognition Method of Decorative Openwork Windows with Sustainable Application for Suzhou Traditional Private Gardens in China," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8439-:d:603510
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    References listed on IDEAS

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    1. Xue-Bo Jin & Wei-Zhen Zheng & Jian-Lei Kong & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Seng Lin, 2021. "Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization," Energies, MDPI, vol. 14(6), pages 1-18, March.
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    Cited by:

    1. Yunda Wang & Qiguan Shu & Ming Chen & Xudounan Chen & Shiro Takeda & Junhua Zhang, 2022. "Selection and Application of Quantitative Indicators of Paths Based on Graph Theory: A Case Study of Traditional Private and Antique Gardens in Beijing," Land, MDPI, vol. 11(12), pages 1-21, December.
    2. Linlin Shan & Long Zhang, 2022. "Application of Intelligent Technology in Facade Style Recognition of Harbin Modern Architecture," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    3. Huishu Chen & Li Yang, 2023. "Analysis of Narrative Space in the Chinese Classical Garden Based on Narratology and Space Syntax—Taking the Humble Administrator’s Garden as an Example," Sustainability, MDPI, vol. 15(16), pages 1-22, August.

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