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相关概念视频

Design Example: Alignment of a Road Line Using GIS01:17

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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使用计算效率高的图像处理技术增强青路面隐藏缺陷的GPR功能

Shengjia Xie1,2, Jingsong Chen3, Ming Cai1,2

  • 1Shanghai Road and Bridge Group Co., Ltd., Shanghai 200433, China.

Materials (Basel, Switzerland)
|September 27, 2025
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概括
此摘要是机器生成的。

通过图像处理预处理地面透雷达 (GPR) 数据,增强了对青路面缺陷的超标特征检测. 这提高了计算机视觉算法的准确性和速度,使得缺陷识别更快.

关键词:
青路面是一种青路面.在地面透雷达.图像处理是图像处理的过程.非破坏性测试是指非破坏性测试.

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科学领域:

  • 地质物理学 地质物理学
  • 土木工程 土木工程是指土木工程.
  • 计算机视觉 计算机视觉

背景情况:

  • 穿透地面雷达 (GPR) 数据中的过度反射特征对于识别隐藏的青路面缺陷至关重要.
  • 目前的深度学习方法与原始GPR数据扎,导致不准确的缺陷检测.
  • 增强GPR数据反射功能对于实时,准确的缺陷分析至关重要.

研究的目的:

  • 为GPR数据预处理提出可访问的图像处理方法.
  • 增强超标反射功能,以提高缺陷检测的准确性和速度.
  • 在现有的计算机视觉算法上评估预处理技术的性能.

主要方法:

  • 应用图像处理技术,如Sobel边缘检测和组图等同原始GPR数据.
  • 使用标准图像处理库进行数据预处理.
  • 量化性能使用已识别的超模信号与噪声比率 (RIHSNR) 的区域.
  • 集成的预处理数据与更快的R-CNN和CBAM-YOLOv8模型.

主要成果:

  • 索贝尔边缘检测和Otsu的值显著提高了检测准确度.
  • mAP@0.5从0.65增加到0.85对于更快的R-CNN和0.72增加到0.88对于CBAM-YOLOv8.8.
  • 推断时间减少到30毫秒的更快的R-CNN和25毫秒的CBAM-YOLOv8.
  • 通过RIHSNR证实了增强的超标特征检测能力.

结论:

  • 简单的图像处理方法有效地增强GPR数据用于路面缺陷检测.
  • 预处理原始GPR数据可以提高深度学习模型的性能.
  • 提出的方法为快速准确的实时GPR缺陷分析提供了一条途径.