Jove
Visualize
联系我们

相关概念视频

Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

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

Shape and flow characterization of the pulmonary arteries after the LeCompte maneuver.

JRSM cardiovascular disease·2026
Same author

Home-based respiratory-gated transcutaneous auricular vagus nerve stimulation for rheumatoid arthritis-a feasibility study.

Clinical rheumatology·2026
Same author

Scoping review of the relationship between glyphosate-based herbicide exposures and autism spectrum disorder.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association·2025
Same author

Evaluation of In-Ear and Fingertip-Based Photoplethysmography Sensors for Measuring Cardiac Vagal Tone Relevant Heart Rate Variability Parameters.

Sensors (Basel, Switzerland)·2025
Same author

Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection.

Sensors (Basel, Switzerland)·2025
Same author

Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms.

Sensors (Basel, Switzerland)·2024
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

相关实验视频

Updated: Jun 18, 2026

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

使用计算机视觉和人工智能算法自动化食品重量和内容估计:第二阶段

Bryan Gonzalez1, Gonzalo Garcia2, Sergio A Velastin3,4

  • 1Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile.

Sensors (Basel, Switzerland)
|January 10, 2026
PubMed
概括

这项研究使用人工智能和计算机视觉来估计餐厅的食物份额. 该系统准确地识别食品,并估计重量,提高餐饮服务的效率.

关键词:
人工智能的人工智能是人工智能.计算机视觉 计算机视觉深度学习是一种深度学习.食品重量估计 食品重量估计

更多相关视频

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
06:21

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Published on: February 19, 2021

6.2K
Author Spotlight: Innovative Ice Cream Melting Behavior Analysis Through a Computer Vision System
08:02

Author Spotlight: Innovative Ice Cream Melting Behavior Analysis Through a Computer Vision System

Published on: October 4, 2024

3.0K

相关实验视频

Last Updated: Jun 18, 2026

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K
Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
06:21

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Published on: February 19, 2021

6.2K
Author Spotlight: Innovative Ice Cream Melting Behavior Analysis Through a Computer Vision System
08:02

Author Spotlight: Innovative Ice Cream Melting Behavior Analysis Through a Computer Vision System

Published on: October 4, 2024

3.0K

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 食品科学 食品科学 食品科学

背景情况:

  • 准确量化食品成分对于餐饮服务至关重要.
  • 传统的部分估计方法可能是劳动密集型,容易出现错误.

研究的目的:

  • 开发和验证人工智能驱动的系统,用于食品含量识别和份量大小估计.
  • 在自助服务环境中利用计算机视觉技术进行自动化食品量化.

主要方法:

  • 使用YOLO (You Only Look Once) 深度学习架构进行对象检测.
  • 采用RGB和深度摄像头来捕捉食物体积和估计重量.
  • 开发了特定食品的密度模型,用于准确的体重计算.

主要成果:

  • 在食品含量识别方面获得了0.873的平均平均精度 (mAP).
  • 在重量估计中显示出较低的误差率:大米为5.07%,肉为3.75%.
  • 在现实世界的餐厅环境中验证了系统的可行性和准确性.

结论:

  • 拟议的AI和计算机视觉系统为自动化食品量化提供了可行和准确的解决方案.
  • 这项技术可以显著提高食品餐饮服务的效率和精度.
  • 在营养分析和库存管理方面可能有进一步的应用.