Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

DOI: http://dx.doi.org/10.15439/2025F7486

Citation: Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 43, pages 327–332 (2025)

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Abstract. Automatic recognition of features in digital images has become a central topic in the field of cultural heritage diagnostics. AI-based models are being increasingly applied to the analysis of infrared reflectography and thermographic data. They show great promise in automating time-consuming manual analyses and improving the objectivity and repeatability of diagnostic assessments. This work proposes 4 specialized classifiers for nails and detachments in work of Arts. In-situ active thermography measurements are used for training proposed models. AI models for nail classification reached accuracy of 96.03 \% and 93.65 \% using planar composite thermal images and volumetric raw data as inputs, respectively. AI models for detachment classification reached accuracy of 87 \% and 57 \% using planar composite thermal images and volumetric raw data as inputs, respectively.

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