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相关概念视频

Microcracking in Concrete01:20

Microcracking in Concrete

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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...
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Design Example: Alignment of a Road Line Using GIS

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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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相关实验视频

Updated: Jun 13, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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USSC-YOLO:用于无人机图像的增强多级道路裂纹物体检测算法

Yanxiang Zhang1, Yao Lu2, Zijian Huo3

  • 1College of Civil Engineering, Central South University of Forestry & Technology, Changsha 410004, China.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种轻量级的USSC-YOLO算法,用于使用无人机 (UAV) 检测道路裂. 改进后的模型提高了道路裂监测的检测精度和效率,确保了交通安全.

关键词:
这是YOLOv5s.深度学习是一种深度学习.智能管理 智能管理机器视觉 机器视觉 机器视觉多个尺度的多个尺度.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 传统的道路裂检测方法阻碍了交通流动.
  • 准确的道路裂检测对于车辆安全至关重要.

研究的目的:

  • 开发使用无人机 (UAV) 的高精度,高效的道路裂检测算法.
  • 改进各种规模的道路裂检测,解决复杂的背景干扰和计算成本.

主要方法:

  • 拟议的USSC-YOLO算法集成了ShuffleNet V2块,协调注意力 (CA) 机制和Swin变压器.
  • 用ShuffleNet V2替换了YOLOv5s的骨干,以减少计算开销.
  • 内置的CA机制以减轻背景干扰和减少检测错误.
  • 添加了Swin变压器块,以增强小裂的检测.

主要成果:

  • 与YOLOv5s相比,USSC-YOLO显示了减少的GFLOP (每秒千兆浮点运算)
  • 在UNFSRCI数据集上实现了mAP@50的6.3%增加和mAP@[50:95]的12%改善.
  • 该模型轻量级,但为基于无人机的道路裂识别提供了出色的检测性能.

结论:

  • USSC-YOLO算法为道路裂检测提供了一个计算效率高,准确的解决方案.
  • 这项技术有助于智能道路管理和维护优先级.
  • 未来的工作重点是评估道路安全,并根据裂检测数据优化维护时间表.