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GFI-YOLOv8:基于YOLOv8的Sika鹿姿势识别目标检测方法

He Gong^1,2,3, Jingyi Liu¹, Zhipeng Li⁴

¹College of Information Technology, Jilin Agricultural University, Changchun 130118, China.

Animals : an open access journal from MDPI

|September 28, 2024

概括

这项研究介绍了GFI-YOLOv8,这是一种用于Sika鹿姿势识别的先进算法,改进了动物福利监测. 该模型达到91.6%的准确性,提高了大规模繁殖操作中的健康评估.

科学领域:

计算机视觉计算机视觉
动物科学动物科学
人工智能的人工智能

背景情况:

锡卡鹿养殖需要精确的健康监测,以确保福利和生产力.
传统的监测方法可能是侵入性的,可能会破坏动物的自然行为.
通过目标检测识别的姿势识别提供了一种非侵入性的方法来评估动物的福祉.

研究的目的:

开发一种用于远程,非侵入性Sika鹿姿势识别的新算法.
为了提高监测Sika鹿健康和行为的准确性和效率.
提高大型鹿繁殖活动中的动物福利标准.

主要方法:

提出了GFI-YOLOv8算法,这是YOLOv8的增强,用于Sika鹿的姿势检测.
集成的iAFF代注意力功能融合模块和AIFI模块.
引入了一种新的卷积神经网络模块,注意力机制,金字塔网络和优化的检测头.

主要成果:

在Sika鹿的姿势识别中,GFI-YOLOv8实现了91.6%的准确性.
与YOLOv8n.相比,显示精度提高了6%,mAP50增加了4.6%.
在关键指标上表现优于其他YOLO系列模型 (YOLOv5n,YOLOv7-tiny,YOLOv8n,YOLOv8s,YOLOv9,YOLOv10).

结论:

该GFI-YOLOv8算法提供了准确和快速识别Sika鹿的姿势.
该模型适用于复杂的,全天候的繁殖环境中的实时监控.
这项技术有助于改善鹿行业的动物福利和管理.

关键词:

注意力机制注意力机制深度学习是一种深度学习.对象检测检测对象检测对象检测西卡鹿鹿是什么意思

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