一种基于改进的FESS-YOLOv8n神经网络的羊行为识别方法.
Xiuru Guo1,2, Chunyue Ma1,2, Chen Wang1,2
1College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China.
Animals : an open access journal from MDPI
|March 28, 2025
概括
一个新的AI模型,Fess-YOLOv8n,增强了绵羊行为检测,以改善牲畜健康监测. 与传统方法相比,这种轻量级模型提供了更好的准确性和实时性能.
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:
- 绵羊养殖在中国北部至关重要,需要有效的健康监测.
- 传统的绵羊行为检测方法 (手动观察,接触设备) 缺乏实时性能和准确性,用于大规模操作.
研究的目的:
- 开发一种先进的,轻量级的绵羊行为检测模型,以加强健康监测.
- 提高自动化羊行为分析的准确性和实时能力.
主要方法:
- 提出了Fess-YOLOv8n模型,一个增强的YOLOv8n神经网络.
- 集成的FasterNet结构和选择性通道下方采样 (SCDown) 用于模型轻量化.
- 集成高效的多尺度注意力 (EMA) 和空间通道协同注意力 (SCSA) 模块,以提高识别性能.
主要成果:
- 这款Fess-YOLOv8n型号的模型大小减少了2.56 MB.
- 在自己构建的数据集上,检测准确度增加了4.7%.
- 成功为大规模的绵羊行为检测提供技术支持.
结论:
- 在大型农场,Fess-YOLOv8n为准确高效地检测绵羊行为提供了可行的解决方案.
- 该模型通过自动行为分析为先进的绵羊健康监测系统奠定了基础.
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