DMSF-YOLO:牛行为识别算法基于动态机制和多尺度特征融合
Changfeng Wu1,2,3, Jiandong Fang1,2,3, Xiuling Wang1,2,3
1College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
这项研究介绍了DMSF-YOLO,这是一种先进的算法,用于识别乳牛的行为,如说谎,站立和吃饭. 该模型准确地识别了复杂农场环境中的多种牛活动,改善了疾病预防和群体管理.
科学领域:
- 计算机视觉和机器学习
- 动物科学与畜牧业 动物科学与畜牧业
- 农业技术 农业技术
背景情况:
- 奶牛的行为是健康和福利的关键指标,对于及时的疾病干预和农场管理至关重要.
- 复杂的农场环境对行为识别提出了挑战,原因是背景噪音,多尺度的行为变化,类似的行动和小目标检测困难.
研究的目的:
- 开发一种新的算法,DMSF-YOLO,用于在现实农场条件下准确和快速识别多种奶牛行为.
- 提高模型处理多尺度特征,背景干扰和区分类似行为的能力.
主要方法:
- 拟议的DMSF-YOLO算法集成了动态机制和多尺度特征融合,用于行为识别.
- 引入了多尺度特征融合卷积 (MSFConv) 模块,以提取和融合不同尺度的特征.
- 设计的C2BRA模块具有双层路由注意力机制,用于动态特征提取和背景抑制.
- 集成的动态头检测头可改善尺度,空间和特定任务的感知,以增强特征提取.
主要成果:
- DMSF-YOLO模型在定制数据集上显示了显著的改进,增加了2.4%的精度 (P),3%的回忆 (R),1.6%的mAP50和2.7%的F1得分.
- 实现了高每秒 (FPS),表明高效的实时处理能力.
- 有效地抑制背景干扰,动态提取多尺度特征,并改进了对小目标和类似行为的检测.
结论:
- DMSF-YOLO算法显著提高了乳牛在复杂环境中的行为识别的准确性和整体性能.
- 该模型能够处理多个尺度的特征,背景噪音和类似的行为,使其适合于乳制品农场管理中的实际应用.
- 这项技术为自动监测提供了强大的工具,使得及时干预和改善动物健康和福利.
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