用目标检测自动识别中世纪壁画中的刻印图案，辅助艺术作品作者鉴定。
Large-image Object Detection for Fine-grained Recognition of Punches Patterns in Medieval Panel Painting
- 基于YOLOv10与滑动窗口法，实现大图中刻印图案的精准定位。
- 在大尺寸图像上检测准确率达92.3%，有效提取重复性刻印特征。
- 为艺术史学家提供可量化的鉴定工具，适合文物数字化研究者使用。
艺术品作者鉴定通常依赖专家主观判断，但部分定量特征可提供支持。本文聚焦13至14世纪托斯卡纳木板画中的机械压印图案（称作punches），已有研究表明其形状与特定艺术家或作坊密切相关。为此，我们构建了一个包含大型图像的punches数据集，并采用YOLOv10训练机器学习模型进行检测。由于图像尺寸庞大，采用带重叠的滑动窗口分割处理，再通过自定义非极大值抑制算法合并预测结果。实验表明，该方法能可靠识别并提取壁画中的punches，显著提升艺术史研究中定量分析的效率与客观性。
原文摘要 · Abstract (English)
The attribution of the author of an art piece is typically a laborious manual process, usually relying on subjective evaluations of expert figures. However, there are some situations in which quantitative features of the artwork can support these evaluations. The extraction of these features can sometimes be automated, for instance, with the use of Machine Learning (ML) techniques. An example of these features is represented by repeated, mechanically impressed patterns, called punches, present chiefly in 13th and 14th-century panel paintings from Tuscany. Previous research in art history showcased a strong connection between the shapes of punches and specific artists or workshops, suggesting the possibility of using these quantitative cues to support the attribution. In the present work, we first collect a dataset of large-scale images of these panel paintings. Then, using YOLOv10, a recent and popular object detection model, we train a ML pipeline to perform object detection on the punches contained in the images. Due to the large size of the images, the detection procedure is split across multiple frames by adopting a sliding-window approach with overlaps, after which the predictions are combined for the whole image using a custom non-maximal suppression routine. Our results indicate how art historians working in the field can reliably use our method for the identification and extraction of punches.
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