基于YOLOv5的玉米籽粒目标检测方法
Kernel Detection Method of Maize Based on YOLOv5
李毅 1谢一民 1樊智慧 1闫伟茉1
作者信息
- 1. 东北农业大学,黑龙江 哈尔滨 150000
- 折叠
摘要
玉米是我国三大主粮作物之一,其表型参数的获取在作物遗传解析和新品种选育等研究领域意义重大.目前,玉米考种主要依靠肉眼和传统机器视觉方法完成,为提高玉米籽粒识别的速度和精度,建立了基于YOLOv5的籽粒目标检测模型,用不同环境下采集的数据训练并选择最优模型.试验结果表明,YOLOv5s的时间复杂度最低,其精确率、召回率和mAP@0.5的均值分别达到90.4%,85.9%和91.4%,实现了对密集黏连目标较理想的检测效果.
Abstract
Currently,kernel detection relies mainly on the naked eye and traditional machine vision,in order to improve the speed and accuracy of kernel detection,this paper established a target detection model based on YOLOv5,trains and selects the optimal model with data collected in different environments.The experimental results showed that YOLOv5s has the lowest time complexity,with an average precision,recall and mAP@0.5 of 90.4%,85.9%and 91.4%,which achieved a ideal detection effect for dense adhe-sion targets.
关键词
YOLOv5/玉米/无损检测/自动化方法Key words
YOLOv5/maize/non-destructive detection/automated methods引用本文复制引用
出版年
2024
NETL
NSTL
万方数据