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

Force Classification01:22

Force Classification

1.0K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.0K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

5.0K
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...
5.0K
Detection of Black Holes01:10

Detection of Black Holes

2.1K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.1K
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

268
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
268
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

378
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
378
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

332
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
332

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

LINC01116 promotes tumor proliferation and neutrophil recruitment via DDX5-mediated regulation of IL-1β in glioma cell.

Cell death & disease·2020
Same author

Metal-Organic Framework Membrane Nanopores as Biomimetic Photoresponsive Ion Channels and Photodriven Ion Pumps.

Angewandte Chemie (International ed. in English)·2020
Same author

Graphdiyne oxide: a new carbon nanozyme.

Chemical communications (Cambridge, England)·2020
Same author

Egg and egg-sourced cholesterol consumption in relation to mortality: Findings from population-based nationwide cohort.

Clinical nutrition (Edinburgh, Scotland)·2020
Same author

A blood-based 22-gene expression signature for hepatocellular carcinoma identification.

Annals of translational medicine·2020
Same author

Identification of Four Pathological Stage-Relevant Genes in Association with Progression and Prognosis in Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis.

BioMed research international·2020

相关实验视频

Updated: May 14, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K

YOLO-PEL:基于YOLO算法的高效和轻量级车辆检测方法

Zhi Wang1, Kaiyu Zhang1, Fei Wu1

  • 1School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201602, China.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了YOLOv8-PEL,这是一种增强的车辆检测模型,可以提高固定摄像头系统的实时性能和效率. 它在减少计算资源的情况下实现了高精度,使其成为受限制应用的理想选择.

关键词:
这是一个YOLO YOLO.轻量级的轻量级的轻量级的轻量级的多个尺度检测检测多个尺度检测对象检测检测对象检测对象检测

更多相关视频

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

1.3K
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

8.9K

相关实验视频

Last Updated: May 14, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K
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

1.3K
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

8.9K

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:

  • 车辆检测对于智能运输系统至关重要.
  • 固定摄像头系统经常面临检测准确性,成本和实时性能之间的权衡.
  • 现有的模型可能会在各个尺度上与特征融合和泛化作斗争.

研究的目的:

  • 开发一种高效准确的车辆检测模型,用于资源有限的实时应用.
  • 增强YOLOv8n模型,以提高功能适应性和多尺度集成.
  • 为了应对极端样本导致的车辆检测精度挑战.

主要方法:

  • 在YOLOv8n.引入了C2F-PPA模块,用于在YOLOv8n.中增强功能融合.
  • 拟议的ELA-FPN用于精细的多尺度特征融合和泛化.
  • 整合了Wise-IoUv3损失功能,以提高检测准确度.
  • 在COCO-Vehicle和VisDrone2019数据集上训练和评估模型.

主要成果:

  • 在COCO-车辆数据集上,YOLOv8-PEL实现了66.9%的mAP@0.5.
  • 该模型拥有2.23M参数,7.0GFLOPs,4.5MB大小和176.8FPS的推断速度.
  • 与YOLOv8n.相比,实现了参数 (25%),GFLOPs (13%),和模型大小 (25%) 的显著减少.
  • 证明了卓越的计算效率和概括能力.

结论:

  • YOLOv8-PEL提供了检测精度和计算效率的卓越平衡.
  • 该模型非常适合实时和资源有限的车辆检测场景.
  • 拟议的改进有效地改善了特征融合,并减轻了梯度问题,以便精确检测.