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

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.

Key words: supervised machine learning, machine vision, geometric features, decision tree, support vector machine