Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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

Design Example: Alignment of a Road Line Using GIS

42
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...
42
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

23
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...
23

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Biomimetic Macrophage Cell Membrane-Based Nanoparticles for Effective Treatment of Glioblastoma Through Boron Neutron Capture Therapy Combined With Immunotherapy.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Linking GWAS risk genes to transcriptional features of major depressive disorder via in vivo Perturb-seq.

Nature genetics·2026
Same author

Decoding age-stratified mutational landscapes in CNS lymphoma via genomic and survival profiling for precision oncology.

Genes & diseases·2026
Same author

[Ethical risks and regulatory considerations in neurofeedback technology].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2026
Same author

Mapping transcription factor functions in astrocytes using in vivo gain-of-function Perturb-seq.

Science (New York, N.Y.)·2026
Same author

Inhibition of Microglial TRPV1 Ameliorates Brain Injury After Intracerebral Hemorrhage by Suppressing AMPK/PINK1-Mediated Mitophagy.

CNS neuroscience & therapeutics·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 8, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

986

道路缺陷识别和定位方法基于改进的ML-YOLO算法

Tianwen Li1, Gongquan Li1

  • 1School of Geosciences, Yangtze University, Wuhan 430074, China.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种改进的ML-YOLO算法,用于精确检测道路缺陷,改进了传统方法. 新型号为实时道路监控和安全提供了更高的准确性和召回率.

关键词:
改进了YOLOv8的功能对象检测检测对象检测对象检测路面应急检测 路面应急检测空间注意力机制空间注意力机制目标定位定位目标定位定位

更多相关视频

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

2.1K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.3K

相关实验视频

Last Updated: Jun 8, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

986
Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

2.1K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.3K

科学领域:

  • 计算机视觉和机器学习
  • 土木工程和基础设施监测 监测

背景情况:

  • 手动道路缺陷检查效率低下,缺乏精确的定位.
  • 现有的自动化方法可能无法完全捕捉复杂的缺陷特征或适应规模变化.

研究的目的:

  • 开发和评估一种增强的ML-YOLO算法,用于准确地识别和定位道路缺陷.
  • 改进基准的YOLOv8对象检测框架,用于道路缺陷分析.

主要方法:

  • 改进了YOLOv8对象检测框架,包括卷积层和空间金字塔聚合.
  • 整合卷积块注意力 (CBA) 进行增强的特征捕获.
  • 集成选择性内核网络 (SKN) 进行自适应性特征提取和优化目标本地化算法.

主要成果:

  • 改进的ML-YOLO算法实现了0.841的检测精度,0.745的回忆精度和0.817的平均精度.
  • 与基线YOLOv8模型相比,显著改善,精度增加 (+0.13),回忆力增加 (+0.117),平均精度增加 (+0.116).
  • 在公共数据集上展示了强大的概括能力,在平均精度和回忆方面表现优于YOLOv8n.

结论:

  • 拟议的ML-YOLO算法为道路缺陷检测和定位提供了一个高度准确和精确的方法.
  • 这种方法增强了实时道路监控,有助于减少交通事故风险和延长道路使用寿命.
  • 该算法表现出强大的概括性,使其适用于各种道路基础设施监控应用.