| 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。 |
| 关键词: 机器视觉 零件分类 特征提取 支持向量机 |
| DOI：10.19554/j.cnki.1001-3563.2020.23.030 |
| 分类号:TP391.4 |
| 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) |
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| Classification Method of Bearing Caps Contour Based on Machine Vision |
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WANG Xiao-chu, QIU Jie-hao, OUYANG Xiang-bo, JIAN Chuan-xia, FAN Bin-xiang
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(Guangdong University of Technology, Guangzhou 510006, China)
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| Abstract: |
| 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. |
| Key words: machine vision parts classification feature extraction support vector machine |
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