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• 图像处理与计算机视觉 • 上一篇    下一篇

基于 SIFT-SVM 的发动机主轴承盖识别与分类

  

  1. (武汉理工大学机电工程学院，湖北武汉 430070)
  • 出版日期:2020-06-30 发布日期:2020-08-18

Classification of engine main bearing cap parts using SIFT-SVM method

  1. (Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan Hubei 430070, China)
  • Online:2020-06-30 Published:2020-08-18

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摘要： 机械零部件的识别与分类任务是制造业自动化生产线的关键环节。针对发动机主
轴承盖混合清洗后的分类，通过分析发动机主轴承盖零件的实际特征，提出基于SIFT-SVM 的
主轴承盖分类识别方法。该方法首先提取训练数据集图像的所有尺度不变(SIFT)特征向量，采
用K-means 聚类方法，将所有的特征向量聚类成K 个分类，并将其代入词袋模型(BoW)中，
使用K 个“词汇”来描述每一张训练图像，从而得到图像的BoW 描述。且以每张图像的BoW
描述作为训练输入，使用支持向量机(SVM)训练主轴承盖的分类模型。实验结果表明：在标
定的照明条件下，主轴承盖零件的识别率可达100%，单个零件识别时间为0.6 s，验证了该算
法的有效性和高效性。

关键词: 零件识别与分类, 机器视觉, SIFT, 词袋模型, 支持向量机分类器

Abstract: The recognition and classification of mechanical components is a key process on the
manufacturing automation line. In terms of the classification of the mixed and cleaned engine main
bearing cap, through the analysis of the actual characteristics of the main bearing cap parts, the
classification and recognition method for the main bearing cap based on SIFT-SVM was proposed.
The method first extracted all scale-invariant feature transform (SIFT) feature vectors of the training
dataset image, then employed the K-means clustering method to cluster all feature vectors into K
classifications, and substituted the obtained K clustering results into the bag of word model (BoW).
“Vocabulary” was utilized to describe each training image, thereby obtaining a BoW description of
the image. The BoW description of each image served as a training input, and the classification model
for the main bearing cap was trained using a support vector machine (SVM). The experimental results
show that under the calibrated lighting conditions, the recognition rate of the main bearing cap parts
can reach 100%, and the recognition time for a single part was 0.6 seconds, which verified the
effectiveness and efficiency of the algorithm.

Key words: parts recognition and classification, machine vision, scale-invariant feature transform;
word bag model, support vector machine classifier