玉米植物高度自动读取基于改进的YOLOv5轻型模型的测量尺度
Jiachao Li1,2,3, Ya'nan Zhou1,2, He Zhang1,2,4
1Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
PeerJ. Computer science
|August 15, 2024
概括
这项研究开发了一种改进的YOLOv5对象检测模型,用于自动测量玉米植物高度. 这种轻量级模型实现了高精度和高效率,优于农业应用的现有方法.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 准确的玉米植物高度对于作物产量和管理至关重要.
- 传统的高度测量是劳动密集型和低效的.
- 需要使用对象检测进行自动测量.
研究的目的:
- 开发一个轻量级的物体检测模型,用于自动测量玉米植物的高度.
- 提高玉米高度数据收集的效率和准确性.
主要方法:
- 提出了一个改进的YOLOv5模型,将MobileNetv3作为骨干.
- 一个基于规范化的注意模块被集成到子网络中.
- 为了提高性能,EioU损失函数取代了CioU损失.
主要成果:
- 该模型实现了98.6%的平均精度,计算复杂度低 (1.2 GFLOPs) 和内存使用量低 (1.8 MB).
- 记录了一个54.1fps的高检测率.
- 与手动读数相比,该算法显示的相对误差小于0.2厘米,超过了YOLOv5s,YOLOv7和YOLOv8s等其他模型.
结论:
- 开发的轻量级模型为自动化玉米植物高度测量提供了卓越的全面性能.
- 该算法符合实际农业应用的准确性要求.
- 这种方法比传统方法显著提高了数据记录和存储效率.
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