A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification.

ABSTRACT: We approach the task of detecting the illicit movement of cultural heritage from a machine learning perspective by presenting a framework for detecting a known artefact in a new and unseen image. To this end, we explore the machine learning problem of instance classification for large archaeological images datasets, i.e. where each individual object (instance) is itself a class that all of the multiple images of that object belongs. We focus on a wide variety of objects in the Durham Oriental Museum with which we build a dataset with over 24,502 images of 4332 unique object instances. We experiment with state-of-the-art convolutional neural network models, the smaller variations of which are suitable for deployment on mobile applications. We find the exact object instance of a given image

SUBMITTER: Winterbottom T 

PROVIDER: S-EPMC9356139 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature