基于基于YOLOv8在多场景天气中改进的轻量级CBR-YOLO模型的肉牛行为识别研究
Ye Mu1,2,3,4, Jinghuan Hu1, Heyang Wang1
1College of Information Technology, Jilin Agricultural University, Changchun 130118, China.
Animals : an open access journal from MDPI
|October 16, 2024
概括
这项研究介绍了Cattle Behavior Recognition-YOLO (CBR-YOLO),一种用于准确识别数字农业中的牛行为的人工智能模型. 通过精确的监测,先进的系统可以提高农场效率和动物福利.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 动物科学动物科学
背景情况:
- 智能数字农业对于提高现代畜牧业效率至关重要.
- 精确监测牛的行为对于优化农场管理和动物福利至关重要.
研究的目的:
- 引入一个改进的YOLOv8模型,牛行为识别-YOLO (CBR-YOLO),用于准确识别牛行为.
- 制定一个全面的监测系统,以各种条件下对牛的状况进行监测.
主要方法:
- 开发了牛行为识别-YOLO (CBR-YOLO) 基于改进的YOLOv8.
- 集成多目标检测,并产生各种各样的天气条件,以进行可靠的监控.
- 引入了内部MPDIoU损失和多卷积聚焦金字塔模块,用于详细的特征学习.
- 设计了一个轻量级的多尺度功能融合检测头,以提高网络效率.
主要成果:
- 在公牛行为识别中达到90.2%的平均准确率.
- 计算负载减少3.9G浮点数,效率提高7.4%.
- 超过了12个最先进的物体检测模型.
结论:
- CBR-YOLO模型显著提高了牛行为识别准确性和计算效率.
- 在农场部署监测系统可以推进牛群自动化管理,改善动物福利.
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