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使用深度学习的哈比沙文化布类分类
Anteneh Demelash1, Eshete Derb2
1Department of Information Technology, Debre Markos University, Debre Markos, Ethiopia. antenehonline@gmail.com.
Scientific reports
|April 22, 2025
概括
这项研究开发了一个深度学习模型来分类Habesha kemis刺质量. VGG16模型实现了95.72%的准确性,通过数字图像分析保存了埃塞俄比亚的文化遗产.
科学领域:
- 计算机科学 计算机科学
- 数字图像处理 数字图像处理
- 保护文化遗产 保护文化遗产
背景情况:
- 哈比沙克米斯 (Habesha kemis) 是埃塞俄比亚传统的女性服装,其特色是古贾姆,冈达尔,谢瓦,阿盖夫和沃洛等地区独特的各种刺.
- 识别和分类这些复杂的刺设计的质量对于文化保存至关重要.
研究的目的:
- 确定最有效的深度学习模型来识别和分类Habesha kemis刺质量.
- 利用数字图像处理和卷积神经网络 (CNN) 模型进行自动化质量评估.
主要方法:
- 利用数字图像处理技术,包括的边缘检测,局部二进制模式和轮检测,用于图像细分和裁剪.
- 在3270张增强图像 (最初1600张) 的数据集上使用CNN分类器VGG16,VGG19和ResNet50v2.
- 评估模型性能使用图像大小为224x224,128x128和64x64像素的SoftMax分类器.
主要成果:
- VGG16模型取得了最高的准确性,测试数据达到95.72%,培训数据达到99.62%.
- 开发的模型成功地细分和分类了来自不同埃塞俄比亚地区的Habesha kemis刺设计.
- 对VGG19和ResNet50v2模型进行了性能评估,VGG16显示出卓越的分类能力.
结论:
- VGG16 CNN模型非常有效地识别和分类Habesha kemis刺设计的质量.
- 这种方法提供了一种可扩展的方法,通过技术来保护和促进埃塞俄比亚的文化遗产.
- 自动化质量评估可以帮助标准化和欣赏传统埃塞俄比亚织品.
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