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

The Retina01:32

The Retina

The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.
Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...

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

Beef Cattle Behavior Recognition Based on Nighttime Farm Videos via Spatio-Temporal Enhancement and Dynamic Fusion.

Animals : an open access journal from MDPI·2026
Same author

iEnhancer-SKNN: a stacking ensemble learning-based method for enhancer identification and classification using sequence information.

Briefings in functional genomics·2023
Same author

iPro-WAEL: a comprehensive and robust framework for identifying promoters in multiple species.

Nucleic acids research·2022
Same author

CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types.

Bioinformatics (Oxford, England)·2022
Same author

[Karyomorphology research in seven kinds of dandelion in Northeast].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2012
Same author

A silver(I)-catalyzed tandem reaction of 2-alkynylbenzaldoxime with alkylidenecyclopropane.

Organic letters·2012

相关实验视频

Updated: Jul 2, 2026

In Vivo Methods to Assess Retinal Ganglion Cell and Optic Nerve Function and Structure in Large Animals
12:18

In Vivo Methods to Assess Retinal Ganglion Cell and Optic Nerve Function and Structure in Large Animals

Published on: February 26, 2022

10.0K

羊面部检测基于改进的视网膜面部算法

Jinye Hao1, Hongming Zhang1, Yamin Han1

  • 1College of Information Engineering, Northwest A&F University, Xianyang 712100, China.

Animals : an open access journal from MDPI
|August 12, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了改进的RetinaFace算法,用于准确检测绵羊的脸部,提高畜牧管理和食品可追溯性. 该方法在现实条件下实现了高性能,克服了各种照明和角度等挑战.

关键词:
注意力模块的注意力模块.计算机视觉 计算机视觉改进了RetinaFace的功能轻量级的轻量级的轻量级的轻量级的绵羊面部检测 绵羊面部检测

更多相关视频

Intravitreal Injections in the Ovine Eye
03:37

Intravitreal Injections in the Ovine Eye

Published on: July 5, 2022

3.3K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

448

相关实验视频

Last Updated: Jul 2, 2026

In Vivo Methods to Assess Retinal Ganglion Cell and Optic Nerve Function and Structure in Large Animals
12:18

In Vivo Methods to Assess Retinal Ganglion Cell and Optic Nerve Function and Structure in Large Animals

Published on: February 26, 2022

10.0K
Intravitreal Injections in the Ovine Eye
03:37

Intravitreal Injections in the Ovine Eye

Published on: July 5, 2022

3.3K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

448

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 畜牧业管理 畜牧管理

背景情况:

  • 准确的羊标识对于食品可追溯性和防止欺诈至关重要.
  • 绵羊面部检测显示出希望,但由于照明,角度和尺度变化而面临挑战.
  • 现有的方法与现实世界农场条件作斗争.

研究的目的:

  • 开发一种有效和轻量级的羊面部检测方法,可在农场实时应用.
  • 为了提高羊面部检测的准确性和速度.
  • 在各种环境条件下解决当前算法的限制.

主要方法:

  • 提出了一个改进的RetinaFace算法,利用一个增强的MobileNetV3大型骨干,具有可切换的心脏卷积,用于更快的多尺度特征提取.
  • 道和空间注意模块被集成到探测器中,以强调羊的关键面部特征.
  • 该算法在从现实世界绵羊农场场景中收集的数据集上进行了测试.

主要成果:

  • 提出的方法实现了95.25%的F1得分和96.00%的平均精度.
  • 该模型的重量轻,尺寸为13.20M,参数为3.20M.
  • 它的平均处理时间为26.83毫秒,可实现实时检测.

结论:

  • 改进的RetinaFace算法有效地解决了绵羊面部检测方面的挑战,提供了高精度和速度.
  • 这种方法在实际农业环境中为羊的识别提供了可靠的解决方案.
  • 轻量化设计使其适合在实际的绵羊养殖场上部署.