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A Review of Artificial Intelligence as a Tool for Damage Detection in Paintings
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The degradation of works of art constitutes a significant problem for the preservation of cultural heritage. In the case of paintings, the observed alterations can be physical, chemical, or visual, affecting both the integrity and appearance of the artworks. Degradation compromises the authenticity, aesthetic legibility, and historical value of paintings, making the early monitoring of such issues, as well as the development of appropriate conservation and restoration strategies, essential. For an effective approach, the characterisation of the materials and techniques used by the artist, as well as the degradation processes inherent in the materials used, proves to be crucial. In this context, the application of artificial intelligence (AI) emerges as a non-invasive solution capable of detecting and predicting degradation in works of art. This bibliographic review aims to explore existing studies in this field in depth, with special attention to contemporary paintings considered as case studies. The methodology involved a systematic review of peer-reviewed studies, theses, and interdisciplinary databases, using keywords related to the topic under investigation (e.g., “degradation detection,” “artificial intelligence,” “craquelure segmentation”). The results indicate that artificial intelligence enables the early detection of degradations that may not yet be visible to the naked eye while also improving objectivity and consistency in the analysis of complex and irregular patterns typical of paintings. It became evident that there is a significant gap in the literature, regarding studies addressing the potential of AI for degradation detection specific to contemporary paintings. However, these could be a valuable case study given their potential material and technical heterogeneity, as well as their differences from traditional easel paintings.
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Palavras-chave
Artificial intelligence Contemporary paintings Craquelure pattern Machine learning Paint loss Painting degradation Conservation Archaeology Materials Science (miscellaneous)