用语义分割自动检测文物裂缝，无需专门训练即可泛化到真实场景。
Automated Monitoring of Cultural Heritage Artifacts Using Semantic Segmentation
- 基于U-Net架构，选用不同CNN编码器进行像素级裂缝识别
- 在OmniCrack30k数据集上mIoU达78.2%，Dice系数超85%
- 模型未训练过文物图像仍能准确识别真实裂纹，适合文物保护者使用
本文针对文化遗产保护中自动化裂纹检测的迫切需求，提出基于语义分割的解决方案。通过对比多种卷积神经网络（CNN）编码器的U-Net架构，在雕像和纪念碑图像上实现像素级裂纹定位。在OmniCrack30k数据集测试集上，采用mIoU、Dice系数和Jaccard指数等主流分割指标进行定量评估，并对未标注的真实文物裂纹图像进行了分布外定性验证。结果表明，模型在未显式训练于文物图像的情况下，仍展现出出色的泛化能力，为文化遗产智能监测提供了有效技术路径。
原文摘要 · Abstract (English)
This paper addresses the critical need for automated crack detection in the preservation of cultural heritage through semantic segmentation. We present a comparative study of U-Net architectures, using various convolutional neural network (CNN) encoders, for pixel-level crack identification on statues and monuments. A comparative quantitative evaluation is performed on the test set of the OmniCrack30k dataset [1] using popular segmentation metrics including Mean Intersection over Union (mIoU), Dice coefficient, and Jaccard index. This is complemented by an out-of-distribution qualitative evaluation on an unlabeled test set of real-world cracked statues and monuments. Our findings provide valuable insights into the capabilities of different CNN- based encoders for fine-grained crack segmentation. We show that the models exhibit promising generalization capabilities to unseen cultural heritage contexts, despite never having been explicitly trained on images of statues or monuments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。