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

Reducing Line Loss01:18

Reducing Line Loss

150
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
150
Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

47
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...
47
Improving Translational Accuracy02:07

Improving Translational Accuracy

10.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
10.1K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.3K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.3K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.1K
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...
6.1K
Distance Corrections01:15

Distance Corrections

27
To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
27

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

AI-assisted teams outperform AI-led teams but not human-only teams in assessing research reproducibility in quantitative social science.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

TEAD4 Promotes Myogenic Differentiation of Porcine Skeletal Muscle Satellite Cells.

Animals : an open access journal from MDPI·2026
Same author

tBid-Mediated Genetic Ablation of Connective Tissue Cells Reveals Their Key Regulatory Function During Limb Regeneration in Axolotls.

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

Extracorporeal shock wave therapy for post-stroke spasticity: an umbrella review of systematic reviews and meta-analyses.

Frontiers in neurology·2026
Same author

Predicting skeletal fluorosis severity using machine learning across diverse fluoride-exposed populations in China.

Scientific reports·2026
Same author

Interlayer Stacking-Controlled Magnetism in Van der Waals Antiferromagnets.

ACS nano·2026

相关实验视频

Updated: Jun 25, 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

1.0K

YOLOv8-PD:基于YOLOv8n模型的改进的道路损坏检测算法

Jiayi Zeng1, Han Zhong2

  • 1College of information and Network Safety, People's Public Security University of China, Beijing, 100038, China.

Scientific reports
|May 27, 2024
PubMed
概括

这项研究介绍了YOLOv8-PD,一种轻量级的道路损坏检测算法. 它提高了路面困境识别的准确性和效率,提高了道路安全.

关键词:
注意力机制注意力机制幽灵网 (GhostNet) 是一个幽灵网络.作为LSCD的负责人.路面的麻烦使人感到困扰.在YOLOv8-PD中,您可以使用YOLOv8-PD.

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

520
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K

相关实验视频

Last Updated: Jun 25, 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

1.0K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

520
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K

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:

  • 道路安全至关重要,需要准确高效地检测道路损坏.
  • 现有的方法在与多个尺度的路面困境和高计算成本作斗争.
  • 自动检测系统对于及时维护和基础设施管理至关重要.

研究的目的:

  • 开发一个改进的,轻量级的道路损坏检测算法 (YOLOv8-PD),基于YOLOv8n.
  • 提高路面应急检测的性能,解决多层次挑战并减少计算负载.
  • 为自动化道路损坏识别提供强大高效的解决方案.

主要方法:

  • 提出了一种新的BOT模块,用于提取全球信息和处理大跨度裂纹特征.
  • 集成了一个大型可分离内核注意力 (LKSA) 机制,以提高检测准确度.
  • 在子网络中开发了一个C2fGhost块,用于高效地提取复杂损坏的特征.
  • 引入了一个轻量级的共享卷积检测头 (LSCD-Head),以提高功能表达性和减少参数.

主要成果:

  • YOLOv8-PD模型实现了2.3M参数和6.1GFLOPs,将基线量减少了约74%.
  • 与基线相比,在RDD2022数据集上实现了1.4个百分点的mAP改进.
  • 在道路损坏数据集上显示了4.2%的mAP增长,表明了良好的稳定性.

结论:

  • 拟议的YOLOv8-PD算法在轻量级道路损坏检测方面提供了显著的改进.
  • 该方法有效地解决了多个尺度的路面困境,提高了准确性和降低了计算成本.
  • YOLOv8-PD为开发先进的自动路面应急检测系统提供了宝贵的参考.