Machine-vision-based tube measurement is characterized by its accuracy, level of automation, noncontact nature and reliability. However, it cannot classify tube structures automatically. Commercial systems and previous algorithms cannot measure branch tubes due to difficulties of classifying tube structures. Therefore, this paper proposes a method for classifying tube structures. Multiple shape descriptors are used to extract tube structure features. Furthermore, RF classifier is used to distinguish among tube structures after tube features extraction. For efficient and accurate classification, the relative importance of each feature is calculated. Compared to results of ResNet-18 training on tube structures dataset, the precision of proposed method achieves 94% while the other is only 88%; experiments shows good performance on Recall and F-score. We developed a software to verify the method on the basis of the multi-view vision system built by our group, which can rapidly and automatically classify numerous complex tube structures used in engineering field.

基于机器视觉的管道测量的特点是准确性，自动化程度，非接触性质和可靠性。但是，它无法自动对管结构进行分类。由于难以对管结构进行分类，因此商用系统和先前的算法无法测量支管。因此，本文提出了一种管结构的分类方法。多个形状描述符用于提取管结构特征。此外，RF分类器用于在提取管特征后区分管结构。为了进行有效而准确的分类，需要计算每个特征的相对重要性。与ResNet-18在管结构数据集上的训练结果相比，该方法的精度达到94％，而其他方法仅为88％。实验表明，在Recall和F评分上表现良好。