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Transabdominal Ultrasound for Pregnancy Diagnosis in Reeves' Muntjac Deer

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Published on: January 7, 2014

功能提取和自动识别模型构建用于乳牛分娩过程中的头背姿势.

Xia Li^1,2,3, Yifeng Song^1,2,3, Xiaoping An^1,2,3

¹College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.

Animals : an open access journal from MDPI

|September 13, 2025

概括

计算机视觉准确地检测到牛产牛期间的"头背"姿势,有助于及时干预. 这项技术通过监测劳动和识别原生母牛中高风险分娩来提高动物福利.

关键词:

这就是YOLOv8的意义.计算机视觉计算机视觉奶牛牛奶牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛出生过程分娩过程.

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科学领域:

兽医科学兽医科学兽医科学
动物行为动物行为
计算机视觉计算机视觉

背景情况:

在牛分娩期间的"头背"姿势是疼痛和分娩的关键指标.
实时监测产犊行为可以改善动物福利,并促进及时援助.

研究的目的:

开发和评估计算机视觉模型,用于分娩期间识别头部姿势.
为了分析在分娩期间原生相对多生母牛的行为差异.

主要方法:

利用在25617张图像上训练的YOLOv8算法来分类牛的姿势.
通过各种相机角度和环境条件 (照明,白天时间) 评估模型性能.
进行了行为分析,比较原生母牛和多生母牛.

主要成果:

对于姿势识别,YOLOv8模型获得了0.71 F1分.
从腹部摄像头角度来看,姿势识别准确度超过了90%,在不同的照明条件下保持稳定.
初代母牛的分娩时间和头背姿势持续时间显著长.

结论:

计算机视觉为实时监控牛分娩提供了一个有前途的工具.
开发的模型可以帮助识别分娩困难并提高动物福利.
在分娩期间,原生母牛和多生母牛之间存在显著的行为差异.