基于ROS和深度学习的杏鲍菇智能分选系统设计

Design of intelligent sorting system of Pleurotus eryngii based on ROS and deep learning

为解决杏鲍菇人工分选耗时低效、准确率低的问题,提出一种基于深度学习的分级检测与抓取检测双模型并联的杏鲍菇智能分选方法和机器人智能分选系统。通过深度相机采集杏鲍菇图像,采用机器人作为分选执行器,基于ROS(Robot Operating System)应用python和C++语言设计开发智能分选控制软件,基于PyQt设计开发监控管理系统。测试结果表明:本分选系统可自动实现杏鲍菇的分级检测和机器人分选抓取,单只杏鲍菇分选检测用时18 ms,分级检测平均准确率为88.35%,机器人抓取成功率为98.33%,智能分选成功率为88.35%,证实系统整体的可行性与有效性,为其他农产品智能分选系统的整体实现提供新的解决思路。

In order to solve the problem of time-consuming,inefficiency and low accuracy of manual sorting of Pleurotus eryngii,this paper proposes an intelligent sorting method and robot intelligent sorting system for Pleurotus eryngii,which is dual-model parallel method of grading detection and grasp detection based on deep learning.The depth camera is used to collect the images of Pleurotus eryngii,and the robot is used as the sorting actuator.The intelligent sorting control software is designed based on ROS(Robot Operating System),python and C++language,and the monitoring and management system is designed based on PyQt.The test results show that the sorting system can automatically realize the grading detection of Pleurotus eryngii and the robot sorting and grasping.The sorting detection of single Pleurotus eryngii takes 18 ms,and the average accuracy of grading detection is 88.35%,the success rate of robot grasping is 98.33%,and the success rate of intelligent sorting is 88.35%,which confirms the feasibility and effectiveness of the system as a whole.It provides a new solution for the whole realization of intelligent sorting system of other agricultural products.