Work

AI classifier of defects in Artworks captured by active infrared thermography

Year: 2025

Type: conference-paper

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.This work was supported by the Slovak Scientific Grand Agency VEGA under the contract 2/0135/23 "Intelligent sensor systems and data processing".Funded by the EU NextGenerationEU through

Source: Annals of Computer Science and Information Systems

DOI: 10.15439/2025f7486

Authors: Peter Malík, Martin Orlej, Branislav Fajčák, Massimo Rippa

Institutions: Slovak Academy of Sciences, Institute of Informatics of the Slovak Academy of Sciences, Slovak University of Technology in Bratislava, National Academies of Sciences, Engineering, and Medicine, Institute of Applied Science and Intelligent Systems, National Research Council

Language: English

Topic: Thermography and Photoacoustic Techniques

Subfield: Mechanics of Materials

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4415420640

FWCI:

Cites: 10

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