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基于深度学习的竹条密度控制技术的研究.

Ziyi Liu1,2, Wenfu Zhang3, Ying Zhao2

  • 1College of Chemical and Materials Engineering, Zhejiang Agriculture and Forestry University, Hangzhou, 311300, China.

Scientific reports
|December 2, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了使用深度学习的自动竹条密度检测. ConvNeXt模型在分类血管捆密度方面达到99%的准确性,改善了质量控制.

关键词:
竹子的密度密度是多少接下来我们来谈谈一下.深度学习是一种深度学习.血管捆绑 血管捆绑

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 传统的竹条质量控制依赖于手工检查,这耗时且主观.
  • 竹子密度是一个关键的质量参数,受血管捆分布的影响.
  • 需要自动化方法来提高竹子密度评估的效率和准确性.

研究的目的:

  • 开发一种基于深度学习的方法,用于自动检测竹条密度.
  • 通过分析横截图图像中的血管束分布来量化竹子密度.
  • 为了比较各种深度学习模型对此任务的性能.

主要方法:

  • 编制了一组由竹条横截面图像组成的数据集.
  • 训练和评估了11个主流深度学习模型,包括卷积神经网络 (CNN) 和变压器.
  • 使用这些模型进行了血管捆密度分类.

主要成果:

  • ConvNeXt模型在血管捆密度分类中表现出卓越的性能.
  • ConvNeXt模型实现了99%的分类准确性.
  • 深度学习模型有效地分析了血管捆分布以量化密度.

结论:

  • 深度学习为竹条密度控制提供了有效和自动化的解决方案.
  • ConvNeXt模型显示了精确的竹子质量评估的巨大潜力.
  • 这项研究强调了人工智能在提高材料质量检查过程中的应用性.