可解释的多流深度学习,用于使用新阿拉伯和非阿拉伯数据集进行细粒度驼品种分类
Hany El-Ghaish1, Dina M Ibrahim2, Amany M Sarhan3
1Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta, 31733, Egypt. dr_h_elghaish@hotmail.com.
Scientific reports
|November 18, 2025
概括
研究人员开发了一种可解释的深度学习模型,用于准确识别驼品种. 这种人工智能系统有效地区分阿拉伯驼和非阿拉伯驼,并识别了五种特定的阿拉伯品种,有助于畜牧管理.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 动物学 动物学
背景情况:
- 驼对干旱的生态系统和沙漠社区至关重要.
- 准确识别驼品种,特别是阿拉伯驼,由于视觉相似性而具有挑战性.
研究的目的:
- 介绍一个新的阿拉伯和非阿拉伯驼图像数据集.
- 为细粒度驼品种分类提出一个可解释的多流深度学习架构.
- 建立一个用于自动化驼品种识别和畜牧管理的基础.
主要方法:
- 收集了1620张驼图像的数据集并进行了注释.
- 开发了一个两阶段的等级适应框架:二元分类 (阿拉伯人与非阿拉伯人) 和多类分类 (五种阿拉伯品种).
- 使用DenseNet121,在线数据增强,类平衡的焦点损失,Adam优化器和Grad-CAM用于可解释性的多流深度学习架构.
主要成果:
- DenseNet121模型在二进制分类中达到98%的准确性,在多类分类中达到76%的准确性.
- 多流设计增强了特征提取和分类准确性.
- 格拉德-CAM可视化为模型的决策过程提供了透明度.
结论:
- 提出的可解释的人工智能模型在识别驼品种方面表现出高准确度.
- 开发的数据集和方法为未来对自动化牲畜识别的研究提供了坚实的基础.
- 这项技术有可能显著改善畜牧管理实践.
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