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Datasets for training and validating a deep learning-based system to detect microfossil fish teeth from slide images.

Kazuhide Mimura^1,2, Kentaro Nakamura^2,3,1

¹Ocean Resources Research Center for Next Generation, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba 275-0016, Japan.

|February 27, 2023

Summary

Researchers developed three datasets for training and validating deep learning models to detect microfossil fish teeth. These datasets support the development of advanced automated methods for identifying fish teeth in paleontological research.

Keywords:

Artificial intelligence Deep learning EfficientNet-V2 Ichthyolith Image classification Machine learning Mask R-CNN Object detection

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Area of Science:

Paleontology
Computer Science
Machine Learning

Background:

Automated detection of microfossil fish teeth is crucial for paleontological research.
Deep learning models offer potential for improving the accuracy and efficiency of this detection process.

Purpose of the Study:

To describe the datasets created for training, validating, and testing deep learning models for microfossil fish teeth detection.
To facilitate the development and evaluation of advanced automated methods for identifying fish teeth.

Main Methods:

Dataset 1: Trained and validated a Mask R-CNN model using 866 training and 92 validation images.
Dataset 2: Trained and validated EfficientNet-V2 models with 17,400 teeth images and 15,036 noise images.
Dataset 3: Evaluated a combined Mask R-CNN and EfficientNet-V2 system using 5177 images with 431 annotated teeth.

Main Results:

The described datasets provide comprehensive resources for deep learning model development.
The datasets enable rigorous testing and validation of automated fish teeth detection systems.

Conclusions:

The curated datasets are essential for advancing automated microfossil fish teeth detection.
These resources will support future research in computational paleontology and machine learning applications.