AI classifier of defects in Artworks captured by active infrared thermography

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.