深度迁移学习在柔性末端执行器端点检测的应用  

Application of Deep Transfer Learning in Endpoint Detection of Flexible End-effector

作　　者：龚炷任 翟敬梅[1] GONG Zhuren;ZHAI Jingmei(School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510000,China)

机构地区：[1]华南理工大学机械与汽车工程学院,广州510000

出　　处：《机械科学与技术》2021年第3期423-427,共5页Mechanical Science and Technology for Aerospace Engineering

摘　　要：针对在工业领域中难以利用小样本数据集训练出准确深度学习模型的问题,提出一种基于迁移学习的深度学习目标点检测方法,对小样本数据集下的柔性末端执行器端点检测。首先利用残差网络(ResNet)构建目标点检测网络;然后利用领域自适应迁移方法构建适应网络,将预训练ResNet-50网络参数转移到柔性末端执行器端点检测模型的训练中,降低深度学习模型训练的难度。实验结果表明,该模型在500张图像训练下就已经具有较好的检测效果,对末端点的定位精度为1.675 mm。In order to solve the problem that it is difficult to train an accurate deep learning model with small sample data set in the industrial field,a deep learning target detection method based on transfer learning was proposed to detect the endpoint of the flexible end-effector under small sample data set.Firstly,the target detection network is constructed using the ResNet;Then the domain adaptive transfer method is used to construct the adaptive network,and the pre-trained ResNet-50 network parameters are transferred to the training process of the endpoint detection model of the flexible end-effector,so as to r educe the training difficulty of the deep learning model.The experimental results show that the model has a good detection effect under the training of 500 images,in which the positioning accuracy of the endpoint is not more than 1.675 mm.

关 键 词：深度学习 迁移学习 末端执行器 末端检测 

分 类 号：TP242.3[自动化与计算机技术—检测技术与自动化装置]