基于智能牧场应用改进的YOLOv10模型的料密度和奶牛养行为多对象检测
Zhiwei Liu1,2,3, Jiandong Fang1,2,3, Yudong Zhao2,3
1College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了BFDet-YOLO,这是一种轻量级模型,用于精确检测智能农场中的奶牛养行为和料密度. 该模型在具有挑战性的环境中增强了视觉感知,提高了农场管理效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 农业技术 农业技术
背景情况:
- 精确的料管理和监测奶牛的养行为对于智能奶牛农场至关重要.
- 视觉感知方面的挑战包括不同的照明,遮蔽和牧场环境中的密集多目标.
研究的目的:
- 提出一个轻量级的多目标检测模型,BFDet-YOLO,用于联合检测奶牛的养行为和料密度.
- 在复杂的牧场环境中解决现有模型的局限性.
主要方法:
- 基于YOLOv10框架开发了BFDet-YOLO,其中有四个关键改进:双向特征融合网络 (BiFPN),轻量化下采样模块 (Adown),注意力增强检测头 (SEAM) 和改进的DIoU损失.
- 通过整合公众和现场数据来构建特定的牧场数据集,以了解奶牛的养行为和料分配.
主要成果:
- 在仅使用1.85M参数的构建数据集上,BFDet-YOLO实现了95.7%的mAP@0.5和70.7%的mAP@0.5:0.95.
- 在检测准确性和稳定性方面表现优于原始YOLOv10和其他主流模型.
结论:
- BFDet-YOLO为智能养系统和智能牧场管理提供了有效和可部署的视觉感知解决方案.
- 该模型在具有挑战性的现实条件下,在检测奶牛和料密度方面表现出卓越的性能.
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