基于深度学习的双动物和手足动物化石的自动识别和形态比较
Jiarui Sun1, Xiaokang Liu1,2, Yunfei Huang3
1State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan, Hubei, China.
PeerJ
|October 16, 2023
概括
这项研究引入了一种自动化方法来识别数百种化石物种,在属级别上达到80%以上的准确性. 这促进了古生物学的研究,使得可以进行大规模的化石分类和分析.
科学领域:
- 古生物学的古生物学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 手动识别化石是耗时的,需要广泛的专业知识.
- 现有的自动化方法仅限于少数物种.
- 双动物和手足类动物在形态,生态和进化上是相关的,这使得它们具有显著的研究兴趣.
研究的目的:
- 开发一种自动化方法来识别数百种化石物种.
- 为训练机器学习模型创建一个全面的数据集.
- 探索计算机视觉在研究化石形态演化的潜力.
主要方法:
- 建立了"双动物和臂足动物化石图像数据集" (BBFID),拥有超过16000个图像标签对.
- 使用EfficientNetV2s架构进行图像分类.
- 采用t分布式随机邻居嵌入 (t-SNE) 来可视化形态特征空间.
主要成果:
- 在属层面 (22个属) 达到>80%的识别精度,在物种层面 (343个物种) 达到~64%的识别精度.
- 成功创建了一个大规模的,分类学确定化石图像数据集 (BBFID).
- 可视化了形态特征,揭示了双动物和臂足动物之间的明显分离.
结论:
- 使用深度学习,可以自动识别数百种化石物种.
- 开发的数据集和方法可以支持大规模的古生物学研究.
- 计算机视觉为研究化石形态演变提供了一个有希望的途径.
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