相关实验视频
Updated: Jul 29, 2026

15:01
Windowing Chicken Eggs for Developmental Studies
Published on: October 1, 2007
基于YOLOv8的子子计数的增强方法和实验研究
Zhenlong Wu1,2, Jikang Yang1,3, Hengyuan Zhang1,3
1College of Engineering, South China Agricultural University, Guangzhou 510642, China.
Animals : an open access journal from MDPI
|March 28, 2025
概括
在家禽养殖场,精确的计数可以通过"你只看一次的计数算法" (YOLO-CCA) 来提高. 这种人工智能模型提高了效率,并降低了大型农业运营中的劳动力成本.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 在大型家禽养殖场手动计数是劳动密集的,昂贵的,容易出错.
- 现有的深度学习模型在密集,封闭的子环境中面临准确性挑战.
研究的目的:
- 为家禽养殖场开发一个准确和高效的自动化计数系统.
- 增强现有的深度学习模型,以提高子环境中的性能.
主要方法:
- 提出了You Only Look Once-Chicken Counting Algorithm (YOLO-CCA),这是一个改进的YOLOv8小型模型.
- 集成的CoordAttention机制和可逆柱网络的骨干成为YOLOv8-small.
- 开发了一种基于值的连续框架检查方法,使用云数据存储.
主要成果:
- YOLO-CCA的F1得分为96.7%,平均精度为80.6%.
- 在真实的家禽养殖环境中,的识别率达到90.9%.
- 在Jetson AGX Orin与TensorRT上的优化部署实现了90.9 FPS的检测速度.
结论:
- 在家禽养殖中,YOLO-CCA显著提高了的计数准确度和效率.
- 该系统降低了劳动力成本,并支持向智能农业转型.
- 开发的算法为在具有挑战性的环境中实现自动化监控提供了强大的解决方案.
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