使用显微镜成像和集体深度学习来分类考古陶的来源
Qian Wang1, Xuan Xiao2,3, Zi Liu1
1Department of Computer, Jing-De-Zhen Ceramic University, Jing-De-Zhen, 333403, China.
Scientific reports
|December 31, 2024
概括
这项研究引入了一种新的集体深度学习模型,用于使用显微镜图像进行考古陶来源分类. 该方法提供了准确,非破坏性的分析,克服了传统技术的局限性.
科学领域:
- 考古学科学 考古学科学
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 传统的陶来源识别方法往往不准确,破坏性或需要昂贵的设备.
- 分析化学或光谱特征可能会损害陶文物的完整性和艺术价值.
- 现有的方法耗时,对文化遗产研究的广泛应用构成局限.
研究的目的:
- 开发一种用于考古陶来源分类的新型,非破坏性方法.
- 克服传统识别技术的局限性,使用显微特征和先进的机器学习.
- 提高陶文化遗迹识别的准确性和效率.
主要方法:
- 构建一个数据集的古代陶显微镜图像.
- 图像预处理技术的应用,包括马校正和CLAHE等级.
- 使用VGG-16,Inception-v3和GoogLeNet深度学习架构进行特征提取,然后通过随机梯度下降 (SGD) 进行最佳融合.
主要成果:
- 拟议的集体深度学习模型实现了卓越的性能,超过了单个深度学习模型.
- 在独立测试数据集上获得了高预测结果:0.9601 (精度),0.9615 (回忆),0.9607 (F1得分) 和0.9583 (准确性).
- 该模型表现出强度和可靠性,性能指标稳定.
结论:
- 开发的集体深度学习模型为非破坏性的考古陶来源分类提供了强大而可靠的解决方案.
- 这种方法克服了传统方法的局限性,提供了更好的诊断准确性和效率.
- 该方法为陶文化遗迹识别和相关研究领域提供了新的见解和工具.
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