摘要
针对管路结构难识别的问题,提出一种基于局部轮廓形状特征的复杂管路结构识别方法,该方法获取管路各结构的轮廓形状,利用形状描述子将各轮廓转换为信号数据,在采集大量信号数据样本的基础上,训练反向传播神经网络,以识别管路结构。训练试验结果表明,所提方法实现了管路结构的自动识别与分割,其准确率达到97%。在实际应用中,采用多相机同时识别,并投票决策的方式提高了识别准确率,实现了分支管路的自动重建与测量。
To solve the problem of automatic tube structure recognition,a recognition method for complex tube structures based on local shape feature was proposed.The recognition method first obtained the different tube structures’shape.Then the obtained shape was converted to a signal by shape descriptor.Batches of signal samples was collected and applied to train a BP neural network to classify the different tube structures.The training experiments showed that the recognition method had accuracy in 97%.In a certain application,multi-cameras were applied to recognize the tube structures and a voting method was also applied to increase the accuracy,which could be applied to reconstruct and measure multi-branch tubes automatically.
作者
黄浩
刘少丽
刘检华
王骁
金鹏
HUANG Hao;LIU Shaoli;LIU Jianhua;WANG Xiao;JIN Peng(Laboratory of Digital Manufacturing,School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China)
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2019年第3期598-606,共9页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(51875044)
国防基础科研资助项目(JCKY2017204B502)~~
关键词
局部轮廓形状
管路结构
管路测量
形状描述子
反向传播神经网络
机器视觉
local shape feature
tube structures
tube measurement
shape descriptor
back propagation
neural network
machine vision
