Bi Haoming, Chen Yelei, Chen Chanjuan, Shu Lei
Digital Art Industry Institute, Hubei University of Technology, Wuhan 430068, China.
NAU-Lincoln Joint Research Center of Intelligent Engineering, Nanjing Agricultural University, Nanjing 210031, China.
Sensors (Basel). 2025 Sep 5;25(17):5558. doi: 10.3390/s25175558.
The Chu-style lacquerware (CSL) dataset is a digital resource specifically developed for the digital preservation and inheritance of Chu-style lacquerware, which constitutes an important component of global intangible handicraft heritage. The dataset systematically integrates on-site photographic images from the Hubei Provincial Museum and official digital resources from the same institution, comprising 582 high-resolution images of Chu-style lacquerware, 72 videos of artifacts, and 37 images of traditional Chinese patterns. It comprehensively demonstrates the artistic characteristics of Chu-style lacquerware and provides support for academic research and cultural dissemination. The construction process of the dataset includes data screening, image standardization, Photoshop-based editing and adjustment, image inpainting, and image annotation. Based on this dataset, this study employs the Low-Rank Adaptation (LoRA) technique to train three core models and five style models, and systematically verifies the usability of the CSL dataset from five aspects. Experimental results show that the CSL dataset not only improves the accuracy and detail restoration of Artificial Intelligence (AI)-generated images of Chu-style lacquerware, but also optimizes the generative effect of innovative patterns, thereby validating its application value. This study represents the first dedicated dataset developed for AI generative models of Chu-style lacquerware. It not only provides a new technological pathway for the digital preservation and inheritance of cultural heritage, but also supports interdisciplinary research in archeology, art history, and cultural communication, highlighting the importance of cross-disciplinary collaboration in safeguarding and transmitting Intangible Cultural Heritage (ICH).
楚式漆器（CSL）数据集是专门为楚式漆器的数字保存和传承而开发的数字资源，楚式漆器是全球非物质手工艺遗产的重要组成部分。该数据集系统整合了湖北省博物馆的现场摄影图像和该机构的官方数字资源，包括582张楚式漆器的高分辨率图像、72件文物的视频以及37张中国传统图案图像。它全面展示了楚式漆器的艺术特色，为学术研究和文化传播提供了支持。数据集的构建过程包括数据筛选、图像标准化、基于Photoshop的编辑与调整、图像修复和图像标注。基于此数据集，本研究采用低秩适应（LoRA）技术训练了三个核心模型和五个风格模型，并从五个方面系统验证了CSL数据集的可用性。实验结果表明，CSL数据集不仅提高了人工智能（AI）生成的楚式漆器图像的准确性和细节还原度，还优化了创新图案的生成效果，从而验证了其应用价值。本研究是首个为楚式漆器AI生成模型开发的专用数据集。它不仅为文化遗产的数字保存和传承提供了新的技术途径，还支持考古学、艺术史和文化传播等跨学科研究，凸显了跨学科合作在保护和传承非物质文化遗产（ICH）中的重要性。
