FSCA-YOLO:一种基于YOLO的增强模型,用于多目标乳牛行为识别
Ting Long1, Rongchuan Yu1, Xu You1
1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Animals : an open access journal from MDPI
|September 13, 2025
概括
这项研究介绍了FSCA-YOLO,这是一个改进的牛行为识别模型,用于奶牛养殖场. 它在复杂的环境中提高了检测准确性,为牲畜监测提供了可靠的基于视觉的解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 动物科学动物科学
- 机器学习 机器学习
背景情况:
- 奶牛养殖中的物体识别模型面临着复杂的背景和母牛遮蔽的挑战,导致检测错误.
- 准确的牛行为识别对于优化奶牛养殖场管理和动物福利至关重要.
研究的目的:
- 开发一个改进的多对象牛行为识别模型,FSCA-YOLO,解决现有系统在现实世界乳制品环境中的局限性.
- 为了提高牛行为识别的准确性和效率,以便在畜牧业监测中实际应用.
主要方法:
- 使用了改进的YOLOv11框架,结合了FEM-SCAM模块与CoordAtt用于特征聚焦以及用于远距离目标的小型物体检测头.
- SIoU损失函数取代了原来的损失函数,以提高识别准确性和融合速度.
- OpenCV 集成用于特定行为识别和区域内计数功能.
主要成果:
- 与基线YOLOv11相比,FSCA-YOLO的性能优于基线YOLOv11,精度为95.7%,回忆率为92.1%,平均精度 (mAP) 为94.5%.
- 与基线相比,该模型显示了1.6%的精度,1.8%的回忆和2.1%的mAP显著改善.
- 改进后的模型在复杂的农业环境中准确地提取牛的特征,证明了其实际用途.
结论:
- FSCA-YOLO提供了一个强大的,可靠的基于视觉的解决方案,用于在具有挑战性的奶牛养殖条件下识别牛的行为.
- 该模型与OpenCV的集成增强了其适应性,用于多牛系统中的多种行为识别和计数需求.
- 这项研究有助于通过改进对象检测和行为分析来推进自动化牲畜监测.
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