用CreToNeXt-YOLOv5进行深度学习策略,用于高级猪脸情绪检测
Lili Nie1, Bugao Li2, Yihan Du1
1College of Information Science and Engineering, Shanxi Agricultural University, Taigu, 030801, Shanxi, China.
Scientific reports
|January 19, 2024
概括
研究人员开发了CreToNeXt-YOLOv5,这是一个先进的猪面部表情识别模型. 这项创新显著提高了检测猪情绪和福祉的准确性,有助于动物福利和畜牧管理.
科学领域:
- 动物行为和动物福利
- 计算机视觉和机器学习
- 农业技术 农业技术
背景情况:
- 面部表情对于理解猪的情绪和福祉至关重要.
- 猪拥有基本的面部肌肉,这使得表情解释具有挑战性.
- 准确识别猪面部表情对于动物福利和畜牧管理至关重要.
研究的目的:
- 引入一个先进的猪面部表情识别模型,CreToNeXt-YOLOv5.5.
- 提高猪面部表情的准确性和检测能力.
- 为改善畜牧业动物福利监测提供一个工具.
主要方法:
- 开发了CreToNeXt-YOLOv5,集成了EIOU损失,协调注意力和CreToNeXt模块.
- 优化了使用EIOU损失进行精确回归的训练动态.
- 整合了协调注意力和CReToNeXt模块,以提高特征灵敏度和细微表达的辨别能力.
主要成果:
- CReToNeXt-YOLOv5实现了89.4%的平均平均精度 (mAP).
- 与基线YOLOv5模型相比,mAP的改善率为6.7%.
- 验证了模型在识别复杂猪面部表情方面的有效性.
结论:
- CReToNeXt-YOLOv5显著提高了猪面部表情识别的准确性.
- 该模型为客观的动物福利评估提供了一个强大的工具.
- 通过加强监测,研究结果支持更人道和明智的畜牧管理实践.
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