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

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

95
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
95
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

99
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
99
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.7K
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...
6.7K

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

KIAA0101 in human cancers: From biomarker discovery to therapeutic targeting.

Biochimica et biophysica acta. Reviews on cancer·2026
Same author

Associations Between Self-Compassion and Behavioural Intention to Receive Seasonal Influenza, Pneumococcal and Respiratory Syncytial Virus Vaccination Among Community-Living Older Adults in Western China: A Population-Based Cross-Sectional Survey.

Vaccines·2026
Same author

Efficient transformer integration in nnU-Net for liver tumor segmentation: an external validation study.

BMC medical imaging·2026
Same author

[Research Advances in Obstructive Sleep Apnea and Lung Cancer Comorbidity].

Zhongguo fei ai za zhi = Chinese journal of lung cancer·2026
Same author

Play together with grandchildren: a potential useful strategy for promoting healthy aging suggested by the evidence of 1,293 Chinese older adults.

BMC geriatrics·2026
Same author

Brain endothelial PTPRO drives LPS-induced metabolic reprogramming and neuroinflammation in sepsis-associated encephalopathy.

Journal of neuroinflammation·2026

相关实验视频

Updated: Jul 26, 2025

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

586

对于连续交通场景的半监控车道检测.

Liwei Deng1, He Cao1, Qingbo Dong1

  • 1School of Automation, Harbin University of Science and Technology, Harbin, China.

Traffic injury prevention
|June 15, 2023
PubMed
概括

这项研究引入了一种新的视频级车道检测算法,用于自动驾驶. 多ERFNet-ConvLSTM模型在复杂的交通场景和不同的速度中提高了准确性和效率.

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 进步的自动驾驶技术需要强大的车道检测.
  • 当前的图像级算法在动态,现实世界的交通场景中面临局限性.

研究的目的:

  • 升级车道检测从图像到视频水平,以增强自动驾驶.
  • 提出一个具有成本效益的算法,能够处理复杂的交通和使用连续图像输入的各种驾驶速度.

主要方法:

  • 推出多ERFNet-ConvLSTM网络框架,集成高效剩余因子化的ConvNet (ERFNet) 和卷积长短期内存 (ConvLSTM).
  • 纳入Pyramidally Attended Feature Extraction (PAFE) 模块来管理多尺度的车道对象.
  • 使用分割数据集进行评估,并进行全面的多维评估.

主要成果:

  • 多ERFNet-ConvLSTM算法在准确性,精度和F1得分方面超过了基准方法.
  • 在复杂的交通场景中表现出卓越的检测能力和在不同驾驶速度的有效性能.

结论:

  • 多ERFNet-ConvLSTM算法为自动驾驶系统中的视频级车道检测提供了一个强大的解决方案.
关键词:
基于CNN-RNN的网络网络.连续的交通场景持续的交通场景.深度学习是一种深度学习.车道检测系统 车道检测系统

更多相关视频

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.4K

相关实验视频

Last Updated: Jul 26, 2025

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

586
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.4K
  • 高性能,降低标签成本和适应各种条件的适应性使其适合于现实世界的应用.