文章摘要
王晓初,邱杰豪,欧阳祥波,简川霞,范彬祥.基于机器视觉的轴承盖外形轮廓分类方法[J].包装工程,2020,41(23):217-222.
WANG Xiao-chu,QIU Jie-hao,OUYANG Xiang-bo,JIAN Chuan-xia,FAN Bin-xiang.Classification Method of Bearing Caps Contour Based on Machine Vision[J].Packaging Engineering,2020,41(23):217-222.
基于机器视觉的轴承盖外形轮廓分类方法
Classification Method of Bearing Caps Contour Based on Machine Vision
投稿时间:2020-04-15  
DOI:10.19554/j.cnki.1001-3563.2020.23.030
中文关键词: 机器视觉  零件分类  特征提取  支持向量机
英文关键词: machine vision  parts classification  feature extraction  support vector machine
基金项目:广东省信息物理融合系统重点实验室项目(2016B030301008);广东工业大学青年基金(17QNZD001)
作者单位
王晓初 广东工业大学广州 510006 
邱杰豪 广东工业大学广州 510006 
欧阳祥波 广东工业大学广州 510006 
简川霞 广东工业大学广州 510006 
范彬祥 广东工业大学广州 510006 
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中文摘要:
      目的 实现曲轴轴承盖在包装生产线上的自动分选,提高生产效率,降低企业生产成本。方法 提出一种基于机器视觉的曲轴轴承盖外形轮廓分类方法,首先等间隔提取预处理曲轴轴承盖图像的行和列,计算每行和每列所含目标像素数量,将关于图像中心对称的2列目标像素数量求和,将提取的特征依序组成对轴承盖正反摆放具有不变性的特征向量;然后采用主成分分析法,对归一化处理的特征向量进行降维;最后采用支持向量机分类。结果 实验结果表明,对样本集的特征向量提取前5个主成分,零件外形轮廓分类准确率达到99.8%。结论 文中所述方法可实现轴承盖零件的准确分类。
英文摘要:
      The work aims to realize automatic sorting of crankshaft bearing caps packaging production line, improve production efficiency and reduce production cost of enterprises. A classification method of crankshaft bearing caps contour based on machine vision was proposed. Firstly, the rows and columns of pre-processed crankshaft bearing cap image were extracted at equal intervals, and the number of target pixels in each row and column was calculated. Then, the number of target pixels in two columns with symmetrical image center was summed. The above extracted features were sequentially composed into feature vectors that were invariant to the positive and negative placement of bearing caps. Then the normalized feature vectors were reduced by principal component analysis. Finally, the support vector machine was used to classify the feature vectors. The experimental results showed that the classification accuracy of contour of parts can reach 99.8% by extracting the first five principal components from the feature vector of the sample set. The method described in this paper can realize the accurate classification of bearing cap parts.
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