一个轻量级的牛安装行为识别系统,基于改进的YOLOv5s
Rong Wang1,2, Ronghua Gao3,4, Qifeng Li5,6
1Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
Scientific reports
|October 13, 2023
概括
这项研究介绍了一种快速,轻量级的牛安装行为检测系统. 该模型实现了高精度和速度,使大型牛场的实时监控成为可能.
科学领域:
- 计算机视觉 计算机视觉
- 动物行为分析.
- 机器学习 机器学习
背景情况:
- 准确检测牛的安装行为对于畜牧管理至关重要.
- 现有的方法在密集环境中经常与速度和模型大小作斗争.
研究的目的:
- 开发一种轻量级的,快速检测牛安装行为的系统.
- 在密集的场景中提高检测速度和模型效率.
主要方法:
- 使用EfficientNetV2概念设计了一个轻量级的骨干网络,其中包括注意力机制,倒置的残余结构和深度可分离的卷积.
- 开发了一个功能增强模块,利用剩余结构,高效的注意力机制和幽灵卷积.
- 将轻量级的骨干和功能增强模块与YOLOv5s集成,以创建牛安装行为识别模型.
主要成果:
- 拟议的模型实现了每秒333.3 (fps) 的推断速度,每图像的推断时间为4.1毫秒.
- 该模型的平均平均精度 (mAP) 为87.7%,超过YOLOv5s的2.1%.
- 与YOLOv5s相比,新型号的速度是0.47倍,重量是2.34倍.
结论:
- 开发的轻量级快速识别模型准确地检测到密集场景中的牛安装行为.
- 该系统的高推断速度和较小的模型重量有利于在大型牛场的全天候实时监控.
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