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Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks.
Emmitt, Joshua; Masoud-Ansari, Sina; Phillipps, Rebecca; Middleton, Stacey; Graydon, Jennifer; Holdaway, Simon.
Afiliación
  • Emmitt J; School of Social Sciences, University of Auckland, Auckland, New Zealand.
  • Masoud-Ansari S; Centre for eResearch, University of Auckland, Auckland, New Zealand.
  • Phillipps R; School of Social Sciences, University of Auckland, Auckland, New Zealand.
  • Middleton S; School of Social Sciences, University of Auckland, Auckland, New Zealand.
  • Graydon J; School of Social Sciences, University of Auckland, Auckland, New Zealand.
  • Holdaway S; School of Social Sciences, University of Auckland, Auckland, New Zealand.
PLoS One ; 17(8): e0271582, 2022.
Article en En | MEDLINE | ID: mdl-35947537
Stone artifacts are often the most abundant class of objects found in archaeological sites but their consistent identification is limited by the number of experienced analysts available. We report a machine learning based technology for stone artifact identification as part of a solution to the lack of such experts directed at distinguishing worked stone objects from naturally occurring lithic clasts. Three case study locations from Egypt, Australia, and New Zealand provide a data set of 6769 2D images, 3868 flaked artifact and 2901 rock images used to train and test a machine learning model based on an openly available PyTorch implementation of Faster R-CNN ResNet 50. Results indicate 100% agreement between the model and original human derived classifications, a better performance than the results achieved independently by two human analysts who reassessed the 2D images available to the machine learning model. Machine learning neural networks provide the potential to consistently assess the composition of large archaeological assemblages composed of objects modified in a variety of ways.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Artefactos Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2022 Tipo del documento: Article País de afiliación: Nueva Zelanda Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Artefactos Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2022 Tipo del documento: Article País de afiliación: Nueva Zelanda Pais de publicación: Estados Unidos