计算机工程与应用2019，Vol.55Issue(22)：225-230,6.DOI:10.3778/j.issn.1002-8331.1807-0168
基于监督式机器学习的零件几何特征智能识别
Intelligent Recognition Method for Geometric Features of Parts Based on Supervised Machine Learning
摘要
Abstract
For the detection of geometric parameters of shell-type parts without fixture positioning in machine vision, it is necessary to identify the geometric features of parts in order to plan the detection path. Therefor this paper proposes an intelligent recognition method for geometric features based on supervised machine learning. Firstly, the feature matrix is constructed according to the relation between features to be identified of the shell parts, and then the supervised machine learning algorithm is used to identify these features. An error correction method based on feature uniqueness is proposed to correct the identification errors generated in the classification process. For the research case involved in this paper, there are 4 holes to be identified in the part, and the accuracy of intelligent recognition is up to 100% after 5 supervised trainings.关键词
监督式机器学习/机器视觉/零件几何特征/决策树/支持向量机Key words
supervised machine learning/machine vision/geometric features/decision tree/support vector machine分类
信息技术与安全科学引用本文复制引用
王玉源,徐杰,吉卫喜..基于监督式机器学习的零件几何特征智能识别[J].计算机工程与应用,2019,55(22):225-230,6.基金项目
江苏省自然科学基金(No.BK20160182). （No.BK20160182）
