用视觉模型分析古币，提升历史文物识别效率与精度。
From Coin to Data: The Impact of Object Detection on Digital Numismatics
- 结合图像与文本的CLIP模型识别古币特征。
- 大模型在复杂图案检测中优于传统方法。
- 适合文化遗产、文物鉴定与防伪研究者。
本文研究先进目标检测技术在数字钱币学中的应用，聚焦历史古币分析。利用对比语言-图像预训练（CLIP）等模型，构建了一个灵活的框架，通过图像与文本描述联合识别和分类特定币面特征。基于两个数据集——包含复杂“圣乔治屠龙”图案的现代俄罗斯硬币，以及带有印度教-佛教符号的公元1世纪东南亚劣质古币——评估不同检测算法在检索与分类任务中的表现。结果表明，大型CLIP模型在复杂图像检测上表现更优，而传统方法在简单几何模式识别中更具优势。此外，提出一种统计校准机制，提升低质量数据集下相似度分数的可靠性。该研究展示了将前沿目标检测技术融入数字钱币学的巨大潜力，可实现历史文物分析的规模化、高精度与高效化，为文化遗产研究、文物溯源及伪造品检测开辟新方法路径。
原文摘要 · Abstract (English)
In this work we investigate the application of advanced object detection techniques to digital numismatics, focussing on the analysis of historical coins. Leveraging models such as Contrastive Language-Image Pre-training (CLIP), we develop a flexible framework for identifying and classifying specific coin features using both image and textual descriptions. By examining two distinct datasets, modern Russian coins featuring intricate "Saint George and the Dragon" designs and degraded 1st millennium AD Southeast Asian coins bearing Hindu-Buddhist symbols, we evaluate the efficacy of different detection algorithms in search and classification tasks. Our results demonstrate the superior performance of larger CLIP models in detecting complex imagery, while traditional methods excel in identifying simple geometric patterns. Additionally, we propose a statistical calibration mechanism to enhance the reliability of similarity scores in low-quality datasets. This work highlights the transformative potential of integrating state-of-the-art object detection into digital numismatics, enabling more scalable, precise, and efficient analysis of historical artifacts. These advancements pave the way for new methodologies in cultural heritage research, artefact provenance studies, and the detection of forgeries.
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