Automated detection of microfossil fish teeth from slide images using combined deep learning models

Mimura, Kazuhide; Minabe, Shugo; Nakamura, Kentaro; Yasukawa, Kazutaka; Ohta, Junichiro; Kato, Yasuhiro

doi:10.1016/j.acags.2022.100092

Cited by 11 publications

(19 citation statements)

References 60 publications

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“…This system was proposed by Mimura et al. [2] and designed to save time and manual work for researchers investigating fossils from deep-sea sediment. In this research, the proposed system significantly reduced false positives by combining an object detection model, ``Mask R-CNN'' [5] , and an image classification model, ``EfficientNet-V2'' [6] .…”

Section: Value Of the Datamentioning

confidence: 99%

“…This system was proposed by Mimura et al. [2] and designed to save time and manual work for researchers investigating fossils from deep-sea sediment.…”

Section: Value Of the Datamentioning

confidence: 99%

“…The datasets add value to the original research paper [2] by helping readers understand through the actual presentation of appropriate images and annotations.…”

Section: Objectivementioning

confidence: 99%

“…However, to measure the performance of the detection system in a more realistic environment, we included all the images taken from a sample in this dataset. Data source location ODP Site 1179 Institution: International Ocean Discovery Program Latitude and longitude: 41°04.79′ N, 159°57.79′ E IODP Site U1366 Institution: International Ocean Discovery Program Latitude and longitude: 26°03.08′ S, 156°53.67′ W MR15-E01 PC 11 Institution: Japan Agency for Marine-Earth Science and Technology Latitude and longitude: 21°58.27′N, 153°47.75′E Data accessibility Repository name: Mendeley Data/Datasets for ichthyolith detection [1] Data identification number: 10.17632/zdpz6m9gzf.1 Direct URL to data: https://data.mendeley.com/datasets/zdpz6m9gzf/1 Related research article [2] K. Mimura, S. Minabe, K. Nakamura, K. Yasukawa, J. Ohta, Y. Kato, Automated detection of microfossil fish teeth from slide images using combined deep learning models, Applied Computing & Geosciences, 16 (2022). 10.1016/j.acags.2022.100092 .…”

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confidence: 99%

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

Mimura

Nakamura

2023

Data in Brief

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Section: Value Of the Datamentioning

confidence: 99%

“…This system was proposed by Mimura et al. [2] and designed to save time and manual work for researchers investigating fossils from deep-sea sediment.…”

Section: Value Of the Datamentioning

confidence: 99%

“…The datasets add value to the original research paper [2] by helping readers understand through the actual presentation of appropriate images and annotations.…”

Section: Objectivementioning

confidence: 99%

mentioning

confidence: 99%

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

Mimura

Nakamura

2023

Data in Brief

Self Cite

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“…Deep learning-based models can not only classify images, but can also identify and locate various objects included within an image. This latter technique, called object detection, has been applied in various academic fields, such as medical image diagnosis [29], cell detection [30], and fossil observation [31], and can perform an automatic advanced image diagnosis with high accuracy, which was previously only possible by skilled observers. Therefore, we aimed to develop a deep learning model that could automatically and accurately detect signals of seafloor hydrothermal activity from MBES images.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Hydrothermal Emission Signatures from Multi-Beam Echo Sounder Images Using Deep Learning

Mimura¹,

Nakamura²,

Takao³

et al. 2022

Preprint

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Seafloor massive sulfide deposits have attracted attention as a mineral resource, as they contain a wide variety of base, precious, and other valuable critical metals. Previous studies have shown that signatures of hydrothermal activity can be detected by a multi-beam echo sounder (MBES), which would be beneficial for exploring sulfide deposits. Although detecting such signatures from acoustic images is currently performed by skilled humans, automating this process could lead to improved efficiency and cost effectiveness of exploration for the seafloor deposits. Herein, we attempted to establish a method for automated detection of MBES water column anomalies using deep learning models. First, we compared the “Mask R-CNN” and “YOLO-v5” detection model architectures, wherein YOLO-v5 yielded higher F1 scores. We then compared the number of training classes and found that models trained with two classes (signal and noise) exhibited superior performance compared with models trained with only one class (signal). Finally, we examined the number of trainable parameters and obtained the best model performance when the YOLO-v5l model with a large trainable parameters was used in the two-class training process. The best model had a precision of 0.928, a recall of 0.881, and an F1 score of 0.904. Using this method, the detection speed was 20−25 ms per frame, which is faster than the pace at which MBES images can generally be generated. Therefore, our best model can be applied in the field for automatic and real-time exploration of seafloor hydrothermal deposits. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

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Artificial intelligence in paleontology

Yu,

Qin,

Watanabe

et al. 2024

Earth-Science Reviews

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Automated detection of microfossil fish teeth from slide images using combined deep learning models

Cited by 11 publications

References 60 publications

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

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

Automated Detection of Hydrothermal Emission Signatures from Multi-Beam Echo Sounder Images Using Deep Learning

Artificial intelligence in paleontology

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