Examining the nuances of Huizhou architecture and building decoration elements within the framework of rural development and urban aesthetics through the application of object detection and explicative analysis
Abstract
This scholarly investigation immerses itself in the intricate domain of Huizhou architecture and building decoration elements, which represent a distinct traditional Chinese architectural style. The study explores the evolution of Huizhou-style architecture and its ornamental techniques while delving into the visual aspects of contemporary urban architecture influenced by this unique style. Employing advanced technologies such as computer vision and explainable artificial intelligence (AI), the research aims to contribute to the preservation and documentation of China’s cultural heritage, specifically focusing on the intricate designs and distinctive building decorations inherent in Huizhou-style architecture. To methodically curate a diverse image dataset, a combination of automated annotation tools and manual labeling was utilized, establishing a robust benchmark for the exploration and comprehension of Huizhou architectural elements. The YOLOv7 model underwent retraining on this dataset, exhibiting noteworthy enhancements in precision, recall, and Mean Average Precision (mAP), surpassing the performance of the pre-trained model. In addition, the study introduces SHAPE, an explainable AI tool designed for interpretability, providing detailed insights into the decision-making process. This not only bolsters the reliability of our results but also enriches our comprehension of Huizhou-style architecture. This multidimensional approach not only propels the field of computer vision but also makes significant strides in the preservation of China’s cultural legacy. By integrating cutting-edge technologies with a meticulous exploration of architectural elements, this research fosters a deeper understanding of Huizhou architecture and its role in shaping the visual landscape of both rural and urban environments.
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DOI: https://doi.org/10.32629/jai.v7i5.1577
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