FewMedical-XJAU:对于细粒度药用植物的分类,一个具有挑战性的基准
Tao Zhang1, Sheng Huang2, Gulimila Kezierbieke1
1College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究引入了用于细粒度植物图像分类 (FPIC) 的新数据集和改进的方法BDCC,提高了复杂环境中罕见药用植物的准确性.
科学领域:
- 计算机科学 计算机科学
- 植物学 植物学
- 机器学习 机器学习
背景情况:
- 现有的细粒度植物图像分类 (FPIC) 数据集缺乏多样性和现实世界的复杂性.
- 挑战包括有限的类别,统一的背景和不足的环境变化.
- 这些限制阻碍了FPIC在实际应用中的有效性.
研究的目的:
- 介绍中国新疆罕见药用植物的FewMedical-XJAU数据集.
- 开发一种改进的FPIC方法 (BDCC),利用文字信息加强歧视.
- 为了应对植物分类中的高类内变异性和类间相似性的挑战.
主要方法:
- 开发了FewMedical-XJAU数据集,具有多种背景,照明和专家注释.
- 拟议的双线深度跨模组合 (BDCC) 将文本前置集成到深度度度度学习中.
- 实现了类意识结构化文本快速构建和动态融合机制.
主要成果:
- FewMedical-XJAU数据集为FPIC提供了一个现实的测试平台.
- 在短时间的分类实验中,BDCC表现出卓越的准确性和稳定性.
- 提出的方法有效地处理复杂的环境条件和植物变异.
结论:
- FewMedical-XJAU数据集和BDCC方法显著提升了细粒度植物图像的分类.
- 这项工作为在具有挑战性的自然环境中识别罕见药用植物提供了强大的解决方案.
- 这些发现支持植物学和保护领域先进的图像分类技术的实际应用.
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