摘要
为使采摘机器人更加快速和准确地识别目标和进行等级分类,提出了一种基于LM优化算法的BP神经网络的目标识别分类方法,能够快速对目标水果进行识别和分类。测试结果表明:采摘机器人目标识别分类方法对测试样本的分类正确率在90%以上,图像识别时间在3.80s以内,能够满足采摘机器人作业需求,证实了该方法的可行性。
In order to make the picking robot recognize and classify the target more quickly and accurately,it proposes a method of target recognition and classification based on BP neural network of LM optimization algorithm,which can recognize and classify the target fruit quickly.The test results show that the accuracy of target recognition and classification method to test samples is more than 90%,and the time of image recognition is less than 3.80s,which can meet the needs of robot operation so as to prove the feasibility of the method.
作者
董珍珍
杨云
Dong Zhenzhen;Yang Yun(Henan Polytechnic Institute, Nanyang 473000, China)
出处
《农机化研究》
北大核心
2022年第8期25-29,共5页
Journal of Agricultural Mechanization Research
基金
教育部科技发展中心第三批“云数融合科教创新”基金项目(2018A10004)。
关键词
采摘机器人
识别分类
LM优化
BP神经网络
picking robot
recognition classification
LM optimization
BP neural network