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\begin{document}
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\title{Applicability of Object Detection to Microfossil Research: Implications
from Deep Learning Models to Detect Microfossil Fish Teeth and Denticles
Using YOLO-v7}



\author[1]{Kazuhide Mimura}%
\author[2]{Kentaro Nakamura}%
\author[3]{Kazutaka Yasukawa}%
\author[4]{Elizabeth Sibert}%
\author[3]{Junichiro Ohta}%
\author[3]{Takahiro Kitazawa}%
\author[3]{Yasuhiro Kato}%
\affil[1]{Chiba Institute of Technology}%
\affil[2]{University of Tokyo}%
\affil[3]{The University of Tokyo}%
\affil[4]{Harvard University}%


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  \date{May 06, 2026}


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\begin{abstract}
Microfossils of fish teeth and denticles, referred to as ichthyoliths,
provide critical information for depositional ages, paleo-environments,
and marine ecosystems, especially in pelagic realms. However, owing to
their small size and rarity, it is time-consuming and difficult to
analyze large numbers of ichthyoliths from sediment samples, limiting
their use in scientific studies. Here, we propose a method to
automatically detect ichthyoliths from microscopic images using a deep
learning technique. We applied YOLO-v7, one of the latest object
detection architectures, and trained several models under different
conditions. The model trained under appropriate conditions with an
original dataset achieved an F1 score of 0.87. We then enhanced the
dataset efficiently using the pre-trained model. We validated the
practical applicability of the model by comparing the number of
ichthyoliths detected by the model with those counted manually. This
revealed that the best model can predict the number of triangular teeth
without manual check, and those of denticles and irregularly shaped
teeth with manual check. This object detection method can extend the
applicability of deep learning to a wider array of microfossils and has
the potential to dramatically increase the spatiotemporal resolution of
ichthyolith records for applications across disciplines.%
\end{abstract}%



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