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相关概念视频

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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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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相关实验视频

Updated: Jul 23, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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使用摄像度的像素级空间信息对结构缺陷的量化.

Youheng Guo1,2, Xuesong Shen1, James Linke2

  • 1School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括

本研究引入了一种新方法,用于精确测量使用3D点云数据和深度学习的小基础设施缺陷. 这种方法准确量化了裂尺寸,改善了结构健康监测和防止故障.

关键词:
卷积神经网络是一种卷积神经网络.裂检测 裂检测 裂检测 裂检测裂纹测量裂纹的测量方法摄影计量 (摄影计量) 是一种摄影仪.

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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
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科学领域:

  • 土木工程 土木工程是指土木工程.
  • 结构健康监测 结构健康监测
  • 计算机视觉 计算机视觉

背景情况:

  • 基础设施老化在全球范围内带来了重大经济和社会风险.
  • 准确量化小缺陷对于防止结构故障至关重要.
  • 现有的方法往往忽略了摄影测量中的3D信息,限制了缺陷测量准确度.

研究的目的:

  • 开发一种高效准确的方法来检测和量化复杂基础设施上的轻微缺陷.
  • 将3D空间信息与深度学习集成在一起,以精确估计缺陷尺寸.
  • 为了克服传统的图像级缺陷分析的局限性.

主要方法:

  • 估计像素大小使用3D点云重建空间信息.
  • 应用深度学习 (卷积神经网络 - CNN) 进行像素级裂检测.
  • 通过将像素数乘以估计的像素大小来计算实际裂纹尺寸.

主要成果:

  • 美国有线电视新闻网 (CNN) 在F1中获得了0.613分的F1分数,以轻微的裂纹提取.
  • 在混凝土路径上进行的一项试点研究表明,在5毫米以下的裂中,平均误差为0.26毫米至0.71毫米.
  • 提出的方法准确地估计了复杂结构上的缺陷.

结论:

  • 开发的方法通过结合3D空间数据,可以精确量化基础设施上的轻微缺陷.
  • 这种方法比传统的缺陷评估方法有显著的改进.
  • 这些发现显示了改善结构健康监测和维护战略的有希望的结果.