PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
October 17, 2025Buildings4 citationsOpen Access

ConvNeXt-L-Based Recognition of Decorative Patterns in Historical Architecture: A Case Study of Macau

View Full Paper
JZJunling ZhouLXLingfeng XiePFPia Fricker

Key Points

  • ConvNeXt-L significantly enhances the recognition and classification of decorative patterns, maximizing efficiency.
  • The study compiled a comprehensive dataset of 11,807 images to improve image classification accuracy.
  • ConvNeXt-L shows superior performance compared to other models, due to its integration of convolution and attention mechanisms.
  • Findings support the preservation and revitalization of historical and cultural heritage in Macau through advanced AI methods.

Abstract

As a well-known World Cultural Heritage Site, the Historic Centre of Macao’s historical buildings possess a wealth of decorative patterns. These patterns contain cultural esthetics, geographical environment, cultural traditions, and other elements from specific historical periods, deeply reflecting the evolution of religious rituals and political and economic systems throughout history. Through long-term research, this article constructs a dataset of 11,807 images of local decorative patterns of historical buildings in Macau, and proposes a fine-grained image classification method using the ConvNeXt-L model. The ConvNeXt-L model is an efficient convolutional neural network that has demonstrated excellent performance in image classification tasks in fields such as medicine and architecture. Its outstanding advantages lie in limited training samples, diverse image features, and complex scenes. The most typical advantage of this model is its structural integration of key design concepts from a Transformer, which significantly enhances the feature extraction and generalization ability of samples. In response to the objective reality that the decorative patterns of historical buildings in Macau have rich levels of detail and a limited number of functional building categories, ConvNeXt-L maximizes its ability to recognize and classify patterns while ensuring computational efficiency. This provides a more ideal technical path for the classification of small-sample complex images. This article constructs a deep learning system based on the PyTorch 1.11 framework and compares ResNet50, EfficientNet-B7, ViT-B/16, Swin-B, RegNet-Y-16GF, and ConvNeXt series models. The results indicate a positive correlation between model performance and structural complexity, with ConvNeXt-L being the most ideal in terms of accuracy in decorative pattern classification, due to its fusion of convolution and attention mechanisms. This study not only provides a multidimensional exploration for the protection and revitalization of Macao’s historical and cultural heritage and enriches theoretical support and practical foundations but also provides new research paths and methodological support for artificial intelligence technology to assist in the planning and decision-making of historical urban areas.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Zhou et al. (2025) studied this question.

synapsesocial.com/papers/68f199c5de32064e504dcf65https://doi.org/10.3390/buildings15203705
Ask AI
Helpful
Bookmark
Share
View Full Paper