Artificial Intelligence-powered fossil shark tooth identification: Unleashing the potential of Convolutional Neural Networks
arxiv(2024)
摘要
All fields of knowledge are being impacted by Artificial Intelligence. In
particular, the Deep Learning paradigm enables the development of data analysis
tools that support subject matter experts in a variety of sectors, from physics
up to the recognition of ancient languages. Palaeontology is now observing this
trend as well. This study explores the capability of Convolutional Neural
Networks (CNNs), a particular class of Deep Learning algorithms specifically
crafted for computer vision tasks, to classify images of isolated fossil shark
teeth gathered from online datasets as well as from the authors' experience
on Peruvian Miocene and Italian Pliocene fossil assemblages. The shark taxa
that are included in the final, composite dataset (which consists of more than
one thousand images) are representative of both extinct and extant genera,
namely, Carcharhinus, Carcharias, Carcharocles, Chlamydoselachus,
Cosmopolitodus, Galeocerdo, Hemipristis, Notorynchus, Prionace and Squatina. We
developed a CNN, named SharkNet-X, specifically tailored on our recognition
task, reaching a 5-fold cross validated mean accuracy of 0.85 to identify
images containing a single shark tooth. Furthermore, we elaborated a
visualization of the features extracted from images using the last dense layer
of the CNN, achieved through the application of the clustering technique t-SNE.
In addition, in order to understand and explain the behaviour of the CNN while
giving a paleontological point of view on the results, we introduced the
explainability method SHAP. To the best of our knowledge, this is the first
instance in which this method is applied to the field of palaeontology. The
main goal of this work is to showcase how Deep Learning techniques can aid in
identifying isolated fossil shark teeth, paving the way for developing new
information tools for automating the recognition and classification of fossils.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要