SMEA-YOLOv8n:一种基于改进的YOLOv8n模型的羊面部表情识别方法
Wenbo Yu1,2, Xiang Yang1,2, Yongqi Liu1,2
1College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Animals : an open access journal from MDPI
|December 17, 2024
概括
这项研究引入了一种改进的羊面部表情识别算法 (SMEA-YOLOv8n),以准确检测疼痛水平. 改进后的模型显著提高了识别准确度,改善了绵羊福利监测.
科学领域:
- 动物科学动物科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 羊的面部表情是疼痛和福祉的关键指标.
- 现有的羊面部表情识别方法面临着诸如低准确度和错误检测等挑战.
研究的目的:
- 为准确的羊面部表情识别开发一个增强的算法.
- 为了更好地监测羊群的福祉,改进对疼痛水平的检测.
主要方法:
- 开发了一个增强的YOLOv8n算法 (SMEA-YOLOv8n),集成SimAM和MobileViTAttention模块.
- 使用效率CIoU损失函数和精细的SPPF模块来改善本地化和特征提取.
- 测试了该算法在识别正常和异常羊面部表情方面的有效性.
主要成果:
- SMEA-YOLOv8n模型实现了92.5%的mAP@0.5,回忆率为91%,精度为86%,F1得分为88.0%.
- 与基线模型相比,观察到显著的改善,正常 (3.7%) 和异常 (5.3%) 表达式的mAP@0.5显著增加.
- 改进的算法在复杂的环境中表现出卓越的性能,减少了错误检测和错误阳性.
结论:
- 拟议的SMEA-YOLOv8n算法有效地提高了绵羊面部表情识别的准确性.
- 这一进步有助于通过更可靠地检测羊的疼痛来改善动物福利监测.
- 整合注意力机制和改进的损失功能为具有挑战性的识别任务提供了强大的解决方案.
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