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

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

4.4K
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...
4.4K
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.5K
2.5K
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
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

1.3K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
1.3K
Classification of Systems-I01:26

Classification of Systems-I

156
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:
156
Reducing Line Loss01:18

Reducing Line Loss

130
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...
130

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

Pollution characteristics, source appointment and environmental effect of oxygenated volatile organic compounds in Guangdong-Hong Kong-Macao Greater Bay Area: Implication for air quality management.

The Science of the total environment·2024
Same author

Extreme environmental doses of diisobutyl phthalate exposure induce oxidative stress and DNA damage in earthworms (Eisenia fetida): Evidence at the biochemical and molecular levels.

Journal of environmental management·2023
Same author

Environmentally relevant concentrations of butyl benzyl phthalate triggered oxidative stress and apoptosis in adult zebrafish (Danio rerio) liver: Combined analysis at physiological and molecular levels.

The Science of the total environment·2022
Same author

Investigation of the mechanism of the reduction of anthracycline-induced cardiotoxicity by Qishen Huanwu Capsule based on network pharmacology.

Annals of palliative medicine·2021
Same author

The Aryl Hydrocarbon Receptor in Chronic Kidney Disease: Friend or Foe?

Frontiers in cell and developmental biology·2021
Same author

Effect of arbuscular mycorrhizal symbiosis on ion homeostasis and salt tolerance-related gene expression in halophyte Suaeda salsa under salt treatments.

Microbiological research·2021

相关实验视频

Updated: May 10, 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

894

基于改进的YOLOv5算法,对机械自动食品包装缺陷检测模型的研究.

Guanyong Liu1, Shuai Zhang1, Lixin Wang1

  • 1Internship and Training Management Office, Binzhou Polytechnic, Binzhou, Shandong, China.

PloS one
|April 24, 2025
PubMed
概括

这项研究引入了一种增强的YOLOv5模型,用于自动检测食品包装缺陷,大大提高了比传统方法的准确性和效率. 这种先进的模型擅长识别微妙的缺陷和小目标,提高工业自动化能力.

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 工业自动化 工业自动化

背景情况:

  • 传统的手动检查食品包装缺陷的方法是低效的,容易出错.
  • 现代工业自动化需要高效率和精确的缺陷检测.
  • 现有的自动化系统难以识别小目标和微妙的缺陷.

研究的目的:

  • 开发基于YOLOv5的增强模型,用于准确有效地检测食品包装缺陷.
  • 改善食品包装中小目标和微妙缺陷的识别.
  • 提高食品包装检查过程的自动化和精度.

主要方法:

  • 集成了一个卷积块注意模块 (CBAM) 来增强特征优先级.
  • 采用金字塔和聚合网络进行多级特征融合以捕捉各种缺陷.
  • 优化了YOLOv5骨干,采用了精简的YOLOv5s模型和自适应空间特征融合 (ASFF),以改善特征混合.

主要成果:

  • 改进的YOLOv5模型实现了卓越的性能,精度 (Ac) =0.96,回忆 (Re) =0.94,F1得分=0.94.
  • 它的性能优于原来的YOLOv5 (Ac=0.82,Re=0.85,F1=0.88),YOLOv5+CBAM (Ac=0.88,Re=0.9,F1=0.89),以及YOLOv5+ASFF (Ac=0.94,Re=0.95,F1=0.94) 的性能.

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
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

1.2K

相关实验视频

Last Updated: May 10, 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

894
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
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

1.2K
  • 组合模型 (CBAM+FPN+PANet+ASFF) 显示了与相关研究工作相比的性能.

    • 结论:

    • 提议的增强型YOLOv5模型显著改善了自动化食品包装缺陷检测.
    • 集成CBAM,多尺度聚变和ASFF有效地解决了检测小微缺陷的局限性.
    • 开发的系统提供了一个强大的解决方案,用于提高食品生产质量控制的自动化和准确性.