基于改进YOLOv7的农业机器人果实智慧检测研究
Intelligent fruit detection of agricultural robots based on improved YOLOv7
任凯麟 1苏力德 1李明煌 1吴浩 2吴楠1
作者信息
- 1. 内蒙古农业大学机电工程学院,内蒙古 呼和浩特 010018
- 2. 宁波大学机械工程与力学学院,浙江 宁波 315211
- 折叠
摘要
为提高果实检测的速度和精度,提出一种基于改进YOLOv7模型的农业机器人果实智慧检测方法.改进后的YOLOv7模型以RepVGG代替CSPDarknet作为骨干网络,并引入ECA(efficient channel attention)机制.以苹果果实为研究对象,采用改进YOLOv7模型进行果实智慧检测.仿真结果表明,所提方法可准确实现农业机器人苹果果实检测,检测准确度、召回率、精确率和平均准确率分别达到 97.67%、95.38%、95.11%和 93.17%;具有较快的检测速度,每张图像的检测时长 11.21 ms.相较于FPN、SSD、Faster R-CNN网络,所提改进YOLOv7模型具有更快的检测速度和更高的检测精度,可用于农业智慧云监控实际应用中,为提高果实检测的速度和精度提供了参考.
Abstract
To improve speed and accuracy of fruit detection,a smart intelligent fruit detection method for agricultural robots based on improved YOLOv7 model was proposed.Backbone network of improved YOLOv7 model was RepVGG instead of CSPDarknet,and ECA(efficient channel attention)mechanism was introduced.Taking apple as research object,and adopting improved YOLOv7 mod-el to fruit intelligent detection.Simulation results showed that the method could accurately achieve agricultural robot apple fruit detection,with detection accuracy,recall,precision,and average accuracy reaching 97.67%,95.38%,95.11%,and 93.17%,respectively,and had a fast detection speed.Detection time for each image was 11.21 ms.Compared with FPN,SSD,and Faster R-CNN model,improved YOLOv7 model had faster detection speed and higher detection accuracy,and could be used in practical applications of agricul-tural intelligent cloud monitoring,providing a reference for improving speed and accuracy of fruit detection.
关键词
果实检测/农业机器人/智慧云监控/YOLOv7/RepVGG/ECAKey words
fruit detection/agricultural robots/intelligent cloud monitoring/YOLOv7/RepVGG/ECA引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据