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

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

Epoxy Composites Reinforced with Sol-Gel Synthesized Alumina-Silica, Alumina, and Natural Silica Fillers: Comparative Mechanical Performance.

Gels (Basel, Switzerland)·2026
Same author

Editorial: Biomechanics of aging: advances in exercise and intervention strategies for older adult wellness.

Frontiers in public health·2026
Same author

A systematic review of machine learning approaches to bovine tuberculosis in cattle.

Research in veterinary science·2026
Same author

Biomineralization of Glucose Oxidase from <i>Aspergillus niger</i> in ZIF-zni for Enhanced Biocatalytic Performance.

Bioengineering (Basel, Switzerland)·2026
Same author

Inverse optimal control of muscle force sharing during pathological gait.

Journal of biomechanics·2026
Same author

The Impact of Energy and Protein Levels on Yellow Mealworm Growth and Chemical Composition.

Insects·2026

相关实验视频

Updated: May 30, 2025

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

多功能废物分类在小批量和灵活的制造业,使用深度学习技术的多功能废物分类.

Arso M Vukicevic1,2, Milos Petrovic3, Nebojsa Jurisevic4

  • 1Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, Kragujevac, Serbia. arso_kg@yahoo.com.

Scientific reports
|January 30, 2025
PubMed
概括
此摘要是机器生成的。

本研究使用分段任何模型 (SAM) 来进行自动废物分类,使灵活的工作站能够快速适应新的废物类型,并具有高精度. 这种方法降低了成本,加快了制造业的部署速度.

关键词:
人工智能的人工智能深度学习 (Deep Learning) 是一种深度学习.制造业 制造业 制造业 制造业回收回收是回收的过程.废物分类 废物分类

更多相关视频

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories
07:52

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Published on: July 10, 2019

14.0K

相关实验视频

Last Updated: May 30, 2025

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
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories
07:52

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Published on: July 10, 2019

14.0K

科学领域:

  • 机器人和自动化机器人与自动化
  • 人工智能的人工智能
  • 环境科学 环境科学

背景情况:

  • 现代制造需要灵活的自动化系统来实现LEAN和小批量生产.
  • 自动化废物分类需要适应多样化和不断变化的废物流.

研究的目的:

  • 评估用于机器人垃圾分类的任何细分模型 (SAM) 家族.
  • 开发一种多功能,两步程序,用于废物自动分离和分类.

主要方法:

  • 使用SAM架构 (SAM,FastSAM,MobileSAMv2,EfficientSAM) 来进行废物提取.
  • 他们使用了分类架构 (MobileNetV2,VGG19,Dense-Net,Squeeze-Net,ResNet,Inception-v3) 来进行分类.
  • 开发了一个两步管道,简化了适应新废物类型的过程.

主要成果:

  • 在对各种废物流 (浮动垃圾,城市垃圾,电子垃圾,智能垃圾桶) 的分类中实现了高精度 (86-97%).
  • 使用MobileNetV2与SAM和FastSAM一起展示了强大的性能.
  • 在多个用例中验证了管道的有效性.

结论:

  • 基于SAM的方法显著减少了对定制算法的需求,降低了成本和开发时间.
  • 这种方法提高了机器人垃圾分类的效率,生产力和准确性.
  • 拟议的程序提供了一个可扩展的解决方案,用于制造和回收过程中的可适应废物管理.