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相关概念视频

Microcracking in Concrete01:20

Microcracking in Concrete

117
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...
117
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

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The rebound hammer test, also known as the Schmidt hammer test, is a non-destructive technique for evaluating the hardness of concrete and, indirectly, the strength of concrete. It operates on the principle that the rebound of a spring-driven mass from a concrete surface correlates to the surface's hardness. The device comprises a mass within a tubular housing, a spring mechanism, and a plunger that strikes the concrete. Upon release, the energy imparted to the mass by the spring causes it...
117
Differential Leveling01:12

Differential Leveling

175
Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
175
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Topographic Surveying and Contours01:29

Topographic Surveying and Contours

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Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
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Methods of Obtaining Topography01:25

Methods of Obtaining Topography

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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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相关实验视频

Updated: Jun 29, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
00:05

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

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基于改进的YOLOv5s的道路表面裂纹检测.

Jiaming Ding1, Peigang Jiao1, Kangning Li1

  • 1Shandong Jiaotong University, Jinan 250357, China.

Mathematical biosciences and engineering : MBE
|March 29, 2024
PubMed
概括

本研究引入了用于道路表面裂检测的增强YOLOv5s算法,比传统方法显著提高了准确性和速度. 先进的模型实现了高精度和快速处理,以有效识别裂.

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:

  • 传统的手动路面裂检测是低效和昂贵的.
  • 需要自动化方法来提高准确性和速度.

研究的目的:

  • 开发一个改进的YOLOv5s算法,用于增强路面裂检测.
  • 为了解决现有方法的低准确度和缓慢速度的局限性.

主要方法:

  • 开发了一个改进的YOLOv5s算法,结合了Res2Net和一个新的多尺度Res2-C3模块.
  • 全球注意力机制 (GAM) 和动态蛇形卷积被集成,以增强特征提取和处理不规则的形状.
  • 修改后的模型接受了道路表面裂识别的训练和评估.

主要成果:

  • 改进后的模型实现了93.9%的平均精度 (mAP),比YOLOv5s.s.提高了12.6%.
  • 检测速度达到了每秒49.97 (FPS).
  • 道路表面裂识别的准确性和速度均显著提高.

结论:

  • 改进的YOLOv5s算法有效地解决了道路表面裂检测中低精度和低速度的挑战.
关键词:
在Res2-C3模块中使用Res2-C3.这是YOLOv5s.注意力机制注意力机制深度学习是一种深度学习.路面裂检测 路面裂检测

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  • 改进后的模型为准确和高效的道路表面检查提供了可行的解决方案.
  • 该研究强调了深度学习在基础设施监控方面的潜力.