Advancing architectural heritage: precision decoding of East Asian timber structures from Tang dynasty to traditional Japan

Abstract

Abstract The convergence of cultural and aesthetic elements in timber structures from China’s Tang Dynasty (618–907 AD) and traditional Japanese architecture provides a rich tapestry of architectural evolution and cross-cultural exchanges. Addressing the challenge of distinguishing and understanding the intricate styles of these structures is significant for both historical comprehension and preservation efforts. This research introduces an innovative approach by integrating the Multi-Head Attention (MHA) mechanism into the YOLOv8 model, enhancing the detection of architectural features with improved precision and recall. Our novel YOLOv8-MHA model not only demonstrates a notable improvement in recognizing intricate architectural details but also significantly advances the state of the art in object detection within complex settings. Quantitative results underscore the model’s effectiveness, achieving a precision of 95.6%, a recall of 85.6%, and a mean Average Precision (mAP@50) of 94% across various Intersection over Union (IoU) thresholds. These metrics highlight the model’s superior capability to accurately identify and classify architectural elements, especially within environments rich with nuanced details, utilizing the enhanced YOLOv8-MHA algorithm. The application of our model extends beyond mere architectural analysis; it offers new insights into the intricate interplay of cultural identity and adaptability inherent in East Asian architectural heritage. The study establishes a solid foundation for the meticulous classification and analysis of architectural styles in timber structures within an expansive cultural and historical context, thereby enriching our understanding and preservation of these traditions.

Keywords

• Cultural heritage conservation
• Historic timber structures
• Tang dynasty architecture
• Japanese traditional architecture
• Object detection
• YOLOv8 algorithm