在养子中的典型异常情况的自动感知,使用计算机视觉
Shida Zhao1,2, Zongchun Bai1,2, Lianfei Huo1,2
1Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China.
Animals : an open access journal from MDPI
|August 10, 2024
概括
这项研究引入了改进的YOLOv8模型,用于检测养子的翻和死亡. 改进的模型实现了更高的准确性和更好的概括性,有助于及时干预子福利.
科学领域:
- 动物科学动物科学
- 计算机视觉 计算机视觉
- 农业技术 农业技术
背景情况:
- 翻转和死亡是子养的重大福利问题.
- 早期发现这些异常对于及时干预和改善动物福利至关重要.
- 现有的检测方法可能缺乏准确性和稳定性,特别是在不同的条件下.
研究的目的:
- 开发一个准确和强大的深度学习模型,用于检测养子的翻和死亡.
- 通过结合注意力机制和改进的损失函数来增强YOLOv8对象检测算法.
- 在现实世界中评估模型的性能,概括能力和效率.
主要方法:
- 修改后的YOLOv8,将GAM注意力机制纳入功能融合项圈.
- 实施Wise-IoU损失函数以平衡数据样本并减少几何参数处罚.
- 使用HRNet-48估计姿势,专注于六个关键的身体点,以进行精细的姿势分析.
- 使用调整的图像亮度 (0.85,1.25) 进行测试,并与主流物体检测算法进行比较.
主要成果:
- 拟议的模型实现了0.924的平均平均精度 (mAP),超过了原来的YOLOv8的1.65%.
- 该模型表现出了出色的概括能力和对照明变化的强度.
- 姿势估计模型实现了0.921的对象关键点相似性 (OKS),每个的处理时间为0.528秒.
结论:
- 改进的YOLOv8模型有效地检测子养子的翻转和死亡异常,准确度高.
- 集成GAM注意力和Wise-IoU显著提高了检测性能和稳定性.
- 开发的姿势估计模型准确地识别出异常姿势,为子福利监测提供了宝贵的工具.
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