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arXiv · 2511.20541

Automated Monitoring of Cultural Heritage Artifacts Using Semantic Segmentation

Andrea Ranieri·Giorgio Palmieri·Silvia Biasotti
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Abstract

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.

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Andrea Ranieri, Giorgio Palmieri, Silvia Biasotti. 2025-11-25. Automated Monitoring of Cultural Heritage Artifacts Using Semantic Segmentation. https://arxiv.org/abs/2511.20541

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