基于改进YOLOv5s的花生仁检测系统
Peanut kernel detection system based on improved YOlOv5s
刘居林 1李德豪 2张振豪 2员玉良2
作者信息
- 1. 青岛市即墨区第一职业中等专业学校,山东青岛 266200
- 2. 青岛农业大学机电工程学院,山东青岛 266109
- 折叠
摘要
为提升对花生仁的检测水平,文中设计了基于改进YOLOv5s的花生仁检测系统.在YOLOv5s神经网络模型引入MobileNet V2模块和CBAM注意力机制后,将其部署到检测系统中.由实验结果可知,部署改进后的神经网络检测系统,检测精度达到98.26％,检测速度提升至改进前的4倍,且权重文件减小了 10.5 MB.由此可见,该花生仁检测系统能实现对花生仁的快速、准确检测.
Abstract
In order to improve the detection level of peanut kernel,a peanut kernel detection system based on improved YOLOv5s is designed in this paper.After introducing MobileNet V2 module and CBAM atten-tion mechanism into YOLOv5s neural network model,it is deployed to the detection system.According to the experiment results,after deploying the improved neural network detection system,the detection accura-cy reaches 98.26％,the detection speed has been increased by 4 times,and the weight file is reduced by 10.5 MB.Thus,it can be seen that the peanut kernel detection system can realize the rapid and accurate detection of peanut kernel.
关键词
花生仁检测/模型改进/花生仁数据集/模型训练/最优模型Key words
peanut kernel detection/model improvement/peanut kernel data set/model training/optimal model引用本文复制引用
出版年
2024
NETL
NSTL
万方数据