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在深度学习中对景观设计和植物选择的分析
Scientific reports
|August 23, 2025
概括
深度学习通过自动化植物叶子图像识别来增强景观设计. 这种新的框架提高了对景观元素的分类和植物选择的准确性,从而推进了智能设计.
科学领域:
- 计算机科学
- 人工智能
- 景观建筑
背景情况:
- 传统的景观设计依赖于人工植物选择和识别.
- 目前的方法在处理复杂的视觉数据方面缺乏效率和准确性.
- 数字化和智能系统为改善景观设计流程提供了潜力.
研究的目的:
- 在景观设计中开发和评估植物叶子图像自动识别的深度学习框架.
- 提高植物选择和景观元素分类的效率和质量.
- 促进景观建筑的数字化和智能化发展.
主要方法:
- 提出了一个整合卷积神经网络 (CNN) 和完全卷积网络 (FCN) 的新框架.
- 集成的多尺度功能融合,注意力机制和物体检测技术.
- 采用域自适应转移学习和区域加权损失函数以提高准确性和稳定性.
主要成果:
- 改进的FCN模型在选流数据集上提高了4.5%的分类准确性.
- 根据拟议的框架,细粒度PLIR的准确性增加了4. 8%.
- 结合物体检测和基于FCN的细分实现了分别90.4%和88.7%的准确性.
结论:
- 深度学习框架显著提高了景观元素分类和植物叶子识别.
- 集成先进的DL技术为智能景观设计提供了强大的解决方案.
- 这项研究为生物多样性保护和可持续城市规划提供了技术支持.
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