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相关概念视频

Microcracking in Concrete01:20

Microcracking in Concrete

111
Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
111

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[Simulation of falling rate stage in drying of TCM pills: mechanisms, models, and applications].

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Identification and Fine-Mapping of <i>qPH15</i> for Plant Height in Sunflower (<i>Helianthus annuus</i> L.).

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Synthesis of 2-Acylpyrroles and 5,6-Dihydro-4<i>H</i>-furo[3,4-<i>c</i>]pyrrol-4-ones via Silver/Base Promoted Intramolecular Hydroalkylation and Oxidation of Propargylamines.

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Multistimuli-Responsive, White-Light-Emitting Cyanostilbene-Cored Dendritic Gels.

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相关实验视频

Updated: Jun 16, 2025

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

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一种基于语义增强的轻量级地面裂快速检测方法.

Bing Yi1, Qing Long2, Haiqiao Liu3

  • 1School of Materials and Chemical Engineering, Hunan Institute of Engineering, 411104, Hunan, China.

Heliyon
|August 16, 2024
PubMed
概括

这项研究引入了一种轻量级的方法,用于快速检测地面裂,增强语义理解. 新方法显著减少了模型大小,同时提高了复杂裂的检测准确度和速度.

关键词:
裂纹检测 裂纹检测 裂纹检测 裂纹检测深度学习是一种深度学习.路面的维护保养路面的维护.语义增强 语义增强 语义增强

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Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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相关实验视频

Last Updated: Jun 16, 2025

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 土木工程 土木工程是指土木工程.

背景情况:

  • 手动检测复杂形状的地面裂是昂贵和低效的.
  • 现有的自动化方法可能缺乏快速部署所需的轻量级设计.

研究的目的:

  • 开发一种轻量级和高效的深度学习模型,用于快速检测地面裂.
  • 增强特征提取和信息融合,以改善裂定位.

主要方法:

  • 提出了一种基于语义增强的新型轻量级地面裂快速检测方法.
  • 集成的上下文引导块进入YOLOv8骨干,以改善功能提取.
  • 利用GSConv和VoV-GSCSP创建一个高效的子网络,用于多功能融合.
  • 优化了检测头,以精确定位目标.

主要成果:

  • 拟议的方法证明了在RDD-2022数据集上有效检测裂纹.
  • 与YOLOv8.8相比,模型参数减少了73.5%.
  • 精度提高了6.6%,F1得分提高了4.3%,每秒 (FPS) 提高了116.6.

结论:

  • 开发的方法比YOLOv8.8轻量化得多.
  • 在检测性能和速度方面提供了显著的改进.
  • 具有用于自动化地面裂监测的重要应用价值.