Automatic identification and morphological comparison of bivalve and brachiopod fossils based on deep learning.

ABSTRACT: Fossil identification is an essential and fundamental task for conducting palaeontological research. Because the manual identification of fossils requires extensive experience and is time-consuming, automatic identification methods are proposed. However, these studies are limited to a few or dozens of species, which is hardly adequate for the needs of research. This study enabled the automatic identification of hundreds of species based on a newly established fossil dataset. An available "bivalve and brachiopod fossil image dataset" (BBFID, containing >16,000 "image-label" data pairs, taxonomic determination completed) was created. The bivalves and brachiopods contained in BBFID are closely related in morphology, ecology and evolution that have long attracted the interest of researchers. W

SUBMITTER: Sun J 

PROVIDER: S-EPMC10576495 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature