基于轻量级YOLOv5的小猪耳朵检测方法的研究和实施
Shujin Qiu1, Yun Li1, Jian Gao1
1College of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, China.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
本研究介绍了一种轻量级的YOLOv5模型,用于在复杂领域实时检测小米耳. 改进的模型实现了高精度和更小的尺寸,使其能够在智能农业的移动设备上部署.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 由于密集的生长,小尺寸和在现场条件下封闭,耳的检测具有挑战性.
- 现有的目标检测模型需要高计算能力,限制了移动设备上的实时部署.
研究的目的:
- 开发一种轻量级和高效的实时目标检测方法,用于小米耳.
- 在复杂的农业环境中提高小米耳检测的准确性和稳定性.
主要方法:
- 通过用MobilenetV3轻量级网络替换其骨干,YOLOv5s模型得到了增强.
- 增加了一个微尺度检测层,以更好地检测小型和封闭的目标.
- 在后处理中使用了合并-NMS技术,以减轻边界模糊效应.
主要成果:
- 改进的模型实现了AP值的97.78%和F1得分的94.20%.
- 模型大小减少到7.56 MB (标准YOLOv5s的53.28%),检测速度得到改善.
- 该模型表现出强大的稳定性和通用性,在Jetson Nano上表现优于经典模型.
结论:
- 轻量级的YOLOv5模型有效地解决了在小米耳检测中密集分布和封闭的挑战.
- 开发的小米检测系统,部署在Jetson Nano上,满足智能农业机械的要求.
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