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

Uncertainty-aware deep kernel learning: An end-to-end approach for crack localization in turbine blades.

Scientific reports·2026
Same author

Policy-driven municipal solid waste network optimization under carbon regulation: a risk-informed MILP framework for a post-conflict recovery city.

Scientific reports·2026
Same author

HybridViT for robust wheat leaf disease detection using CLAHE and attention-based feature fusion.

Scientific reports·2026
Same author

HOG-supervised compact CNNs for real-time visual place recognition.

Scientific reports·2026
Same author

Hyperkinetic Movement Disorder as the First Manifestation of Moyamoya Disease in a 15-Year-Old: A Case Report.

Clinical case reports·2026
Same author

An optimized real-time qualitative HOG-based visual servoing system for autonomous wheelchair.

Scientific reports·2026

相关实验视频

Updated: Jan 10, 2026

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

9.8K

直接基于视频的时空深度学习用于牛的检测.

Md Fahimuzzman Sohan1, Raid Alzubi2, Hadeel Alzoubi2

  • 1Department of Software Engineering, Daffodil International University, Dhaka, 1207, Bangladesh.

Scientific reports
|November 22, 2025
PubMed
概括

本研究引入了一种深度学习框架,用于使用视频分析自动检测牛. 3D卷积神经网络模型实现了90%的准确性,为实时农场应用提供了更简单,更快的替代方案.

关键词:
牛群是牛群,牛群就是牛群.计算机视觉技术的使用方法深度学习是一种深度学习.图像处理 图像处理的检测 的检测

更多相关视频

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

7.2K
Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

13.3K

相关实验视频

Last Updated: Jan 10, 2026

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

9.8K
Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

7.2K
Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

13.3K

科学领域:

  • 兽医医学 兽医医学 兽医医学
  • 人工智能的人工智能
  • 动物科学动物科学

背景情况:

  • 牛严重影响动物福利和农场生产力.
  • 早期和准确地检测是及时干预和减轻经济损失的关键.

研究的目的:

  • 开发和评估一个时空深度学习框架,用于使用视频数据自动检测牛.
  • 为了比较3D卷积神经网络 (3D CNN) 和卷积长短期记忆 (ConvLSTM2D) 的性能.

主要方法:

  • 一套由50个牛视频片段组成的数据集被策划并标记为腿或非腿.
  • 应用了数据增强技术来改善模型通用化.
  • 两个深度学习架构,3D CNN和ConvLSTM2D被训练和评估用于视频分类.

主要成果:

  • 3D CNN模型实现了90%的准确性,精度为92%,回忆率为90%,F1得分为90%.
  • 3D CNN的性能超过了ConvLSTM2D模型 (85%的准确性).
  • 端到端的方法表现出与现有方法可比的准确性,其设计更简单,单阶段.

结论:

  • 时空深度学习,特别是使用3D CNN,对于自动检测牛是有效的.
  • 拟议的框架为多阶段方法提供了一个计算效率更高,更简单的替代方案.
  • 这种方法适合在畜牧环境中实时部署.