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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

103
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
103
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.0K
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...
6.0K
Classification of Systems-II01:31

Classification of Systems-II

137
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
137
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

113
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
113
Classification of Systems-I01:26

Classification of Systems-I

177
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
177
Force Classification01:22

Force Classification

1.2K
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,...
1.2K

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

Deep Learning for Visual Speech Analysis: A Survey.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Offset-FA: A Uniform Method to Handle Both Unbounded and Bounded Repetitions in Regular Expression Matching.

Sensors (Basel, Switzerland)·2022
Same author

Spironolactone Protects against Diabetic Cardiomyopathy in Streptozotocin-Induced Diabetic Rats.

Journal of diabetes research·2018
Same author

Exploring efficient grouping algorithms in regular expression matching.

PloS one·2018
Same author

Metal-Free Decarboxylative Alkoxylation of 2-Picolinic Acid and Its Derivatives with Cyclic Ethers: One Step Construction of C-O and C-Cl Bonds.

Organic letters·2018
Same author

Details of a thallium poisoning case revealed by single hair analysis using laser ablation inductively coupled plasma mass spectrometry.

Forensic science international·2018

相关实验视频

Updated: Jun 13, 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

487

GMS-YOLO:一种算法,用于在复杂的环境中,在狭窄的区间中进行多层次物体检测.

Qixiang Ding1, Weichao Li1, Chengcheng Xu1

  • 1National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
概括

一个新的GMS-YOLO网络通过准确检测诸如松散的紧固件等危险因素,改善了狭窄空间的安全检查. 这种增强的物体检测模型比以前的版本更轻,更准确.

关键词:
这就是YOLOv8的意义.复杂的环境 复杂的环境封闭的隔间是封闭的隔间.多尺度物体检测多尺度物体检测量度轻量化权重 量度轻量化权重

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

8.9K
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

9.4K

相关实验视频

Last Updated: Jun 13, 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

487
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
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

9.4K

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:

  • 狭窄的隔间存在安全隐患 (例如,松散的紧固件,物体侵入) 由于空间有限,使手工检查复杂化.
  • 现有的检查方法在这些狭窄的区域内与复杂的环境,多样化的目标和不同的物体尺寸作斗争.

研究的目的:

  • 开发一个先进的物体检测网络,GMS-YOLO,用于在具有挑战性的封闭区间环境中加强安全检查.
  • 提高在封闭空间内识别潜在危险的准确性和效率.

主要方法:

  • 提出了一个基于YOLOv8框架的新型GMS-YOLO网络,将GhostHGNetv2纳入,以获得更优质的特征提取.
  • 集成的多尺度卷积注意力 (MSCA) 解决不同的目标尺度和共享卷积检测头 (SCDH) 轻量级,高精度检测.

主要成果:

  • 与原始模型相比,GMS-YOLO网络显示参数减少了37.8%,GFLOP减少了27.7%.
  • 在封闭区内的物体检测任务中,平均精度从82.7%提高到85.0%.

结论:

  • GMS-YOLO网络提供了一种轻量级且准确的解决方案,用于在复杂,封闭的环境中检测物体.
  • 拟议的方法通过提高危险识别准确性和效率,显著提高了安全检查能力.