Application of AI Technology to Non-Destructive Analysis of Bronze Rust
Ling Ye Jiang · 2024
In the contemporary era marked by rapid technological advancements, the integration of artificial intelligence (AI) and computer technology unlocks new possibilities for the preservation and analysis of cultural artifacts. The innovative Vision Transformer model offers a novel approach to the non-destructive analysis of bronze rust by classifying images of bronze artifacts. This technology has the potential to significantly enhance the accuracy of artifact classification and exert a profound influence on the conservation and restoration of cultural relics. Notably, conventional destructive analysis methods often compromise the aesthetic value of artifacts. The presented research introduces a methodology that avoids damaging the artifacts. Through the segmentation of corroded areas based on pixel color ranges, significant results have been achieved in identifying the severity of corrosion on artifacts. This contributes to preserving the original state of the artifacts and elevates precision in classifying rust patterns. Essentially, the study pioneers the integration of cutting-edge technology in archaeology and artifact conservation. By providing more effective and accurate non-destructive analysis tools, the work propels advancements in the field, showcasing the potential of AI to revolutionize archaeological practices.