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猪计数算法基于改进的YOLOv5n模型,具有多场景和更少的参数数量
Yongsheng Wang1,2, Duanli Yang1,2, Hui Chen3,4
1College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China.
改进的YOLOv5n算法在复杂的农场环境中提高了猪计数的准确性. 这种优化的模型显著降低了参数和计算负载,使得用于畜牧监测的实用Android应用程序开发成为可能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 农业技术 农业技术
背景情况:
- 准确的猪计数对于大型养猪场管理至关重要.
- 现有的方法面临着诸如遮蔽,不同照明和各种成像条件等挑战.
研究的目的:
- 开发一种高精度的猪计数算法,使模型复杂性降低.
- 为Android系统创建一个实用的猪计数应用程序.
主要方法:
- 创建了一个多场景数据集,以改善模型概括.
- 为了减少参数,YOLOv5n的骨干被FasterNet取代.
- 子使用E-GFPN进行了优化,以增强功能融合.
- 实施了焦点EIoU损失功能,以提高识别准确度.
主要成果:
- 改进的模型实现了97.72%的AP.
- 与YOLOv5n.相比,参数,计算和模型大小分别减少了50.57%,32.20%和47.21%,与YOLOv5n.相比.
- 检测速度达到75.87 f/s,在复杂的环境中提高了准确性和稳定性.
结论:
- 优化的YOLOv5n算法为猪计数提供了卓越的准确性和效率.
- 开发了一个功能性的Android应用程序,证明了它的实际实用性.
- 该方法可扩展到其他牲畜计数应用,显示出广泛的实用价值.
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