Deep learning for a swift non-invasive recognition and delineation of corrosive iron compounds present on the surface of unrestored archaeological artefacts.

The assessment of the degradation state of an unearthed ancient artefact concerns the identification of the material composition and of the corrosive compounds that are present at the surface. The standard investigation makes use of a combination of invasive and non-invasive techniques and complex devices, while it also relies on the extensive experience of the restorer. The current paper puts forward a new possibility of employing an alternative computational solution with the support of deep learning that recognizes and delineates all the corrosive compounds from stereo microscope images of the surface of a metal item. With the input received from a portable microscope, such a fast non-destructive tool would provide straightforward assistance at the excavation site, as well as a second opinion for novice investigators. Iron archaeological objects are considered and four corrosion compounds are identified and outlined by the deep learning models, i.e. Fe 2 O 3 , FeSO 4 , FeCl 3 and FeO. The results show that the deep computational identification and delimitation of the four corrosive types is meticulous even with a minimal annotation provided for training. [ABSTRACT FROM AUTHOR]