Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jun 4, 2025

Micro/Nano-scale Strain Distribution Measurement from Sampling Moiré Fringes
06:56

Micro/Nano-scale Strain Distribution Measurement from Sampling Moiré Fringes

Published on: May 23, 2017

12.2K

基于CA-DeepLabv3的自然纤维微结构检测方法的研究

Shuaishuai Lv1, Xiaoyuan Li1,2, Hitoshi Takagi2

  • 1School of Mechanical Engineering, Nantong University, Nantong 226019, China.

Materials (Basel, Switzerland)
|December 17, 2024
PubMed
概括

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Dual Roles of Ubiquitin-Specific Peptidase 10 (USP10) in Cancer.

Cells·2026
Same author

Bidirectional associations between socioeconomic status, physical activity, and depressive symptoms in middle-aged and older adults: A cross-lagged prospective cohort study.

Journal of affective disorders·2026
Same author

Optimal dose and type of exercise to improve cognitive function in adults with major depressive disorder: a systematic review and Bayesian model-based network meta-analysis.

Frontiers in public health·2026
Same author

Clinical Deployment of Interpretable AI: Bridging Routine Clinical Tests and Proteomic Signatures for Preeclampsia Risk Stratification.

Current drug targets·2025
Same author

Family endowments and multidimensional poverty among urban older adults living alone.

Frontiers in public health·2025
Same author

Metabolic engineering of Escherichia coli for highly efficient N-acetylneuraminic acid production.

Bioresource technology·2025

准确的天然纤维特征需要精确的横截面分析. 这项研究引入了CA-DeepLabv3+模型,用于详细的微观结构细分,提高了性能测量准确度.

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机视觉 计算机视觉
  • 生物技术是生物技术.

背景情况:

  • 天然纤维具有可变的横截面,使精确的属性确定变得复杂.
  • 假设圆形截面引入显著的测量误差.
  • 自然纤维微结构的精确几何特征对于先进的应用至关重要.

研究的目的:

  • 开发一种精确的天然纤维微结构检测和细分方法.
  • 在属性分析中解决非均纤维截面的挑战.
  • 通过先进的深度学习提高自然纤维特征的可靠性.

主要方法:

  • 提出了一种利用CA-DeepLabv3+网络的自然纤维微结构检测方法.
  • 采用了MobileNetV2作为功能提取骨干.
  • 通过级联优化了Atrous空间金字塔聚合 (ASPP) 模块,并集成了一个高效的多尺度注意力 (EMA) 机制.

主要成果:

  • 开发的算法准确地在各种天然纤维类型中对微结构进行细分.
  • 实现了 95.2% 的平均像素精度 (mPA).
  • 在分段精确度方面获得了90.7%的欧盟平均交叉点 (mIoU).
关键词:
在DeepLabv3+中使用.欧洲药物管理局的机制深度学习是一种深度学习.纤维/矩阵结合的结合.这是天然纤维.

更多相关视频

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
07:58

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Published on: November 11, 2020

6.0K
Author Spotlight: Revolutionizing Microfluidics Through Microchannel Fabrication on Nanopaper
03:58

Author Spotlight: Revolutionizing Microfluidics Through Microchannel Fabrication on Nanopaper

Published on: October 6, 2023

1.5K

相关实验视频

Last Updated: Jun 4, 2025

Micro/Nano-scale Strain Distribution Measurement from Sampling Moiré Fringes
06:56

Micro/Nano-scale Strain Distribution Measurement from Sampling Moiré Fringes

Published on: May 23, 2017

12.2K
Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
07:58

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Published on: November 11, 2020

6.0K
Author Spotlight: Revolutionizing Microfluidics Through Microchannel Fabrication on Nanopaper
03:58

Author Spotlight: Revolutionizing Microfluidics Through Microchannel Fabrication on Nanopaper

Published on: October 6, 2023

1.5K

结论:

  • 基于CA-DeepLabv3+的方法提供了自然纤维微结构的准确细分.
  • 这种方法增强了几何信息的提取,导致更可靠的属性测量.
  • 这项研究为分析复杂的天然纤维形态提供了强大的解决方案.