用卷积神经网络对磨损分析中的抛光进行分类
Anastasia Eleftheriadou1, Youssef Djellal2,3, Shannon P McPherron2,4
1Interdisciplinary Center for Archaeology and the Evolution of Human Behaviour (ICArEHB), Universidade do Algarve, Faro, Portugal. aeleftheriadou@ualg.pt.
Scientific reports
|October 22, 2025
概括
深度学习模型对石化使用磨损分析充满希望,有效地识别骨和皮肤的抛光. 定制模型在较小的表面积上表现最好,突出了对更大,更多样化的数据集的需求.
科学领域:
- 石化使用磨损分析
- 考古学是一种科学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 石质使用磨损分析研究使用和沉积的工具痕迹.
- 波兰语分析至关重要,但通过机器学习实现自动化的好处.
- 深度学习在服装分析中的潜力需要进一步研究.
研究的目的:
- 探索深度学习,特别是卷积神经网络 (CNN),用于石化使用磨损分析.
- 为了确定最佳参数,如表面积大小和模型架构.
- 根据接触材料和使用强度对实验抛光进行分类.
主要方法:
- 卷积神经网络 (CNN) 被用来分类实验抛光.
- 分类是基于接触材料 (木材,皮革,骨头) 和使用强度.
- 使用不同的图像补丁大小来比较定制和预训练的CNN模型.
主要成果:
- CNNs在识别骨头和皮肤的抛光时表现出有效性,但对于木材来说不那么有效.
- 模型成功地区分了从短期使用到长期使用的抛光.
- 定制模型的性能优于预先训练的模型,特别是具有较小的图像补丁.
结论:
- 深度学习,特别是CNN,显示了自动化石化使用磨损分析的潜力.
- 最佳的结果取决于模型架构和分析的表面积的规模.
- 扩大数据集和完善CNN工作流程对于未来的进步至关重要.
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