电子测量与仪器学报

刘铭璇,唐东林,何媛媛,周立.CNN 集成机器学习的金属缺陷少样本分类方法[J].电子测量与仪器学报,2023,37(3):86-94

CNN 集成机器学习的金属缺陷少样本分类方法

Classification of metal defects with few-shot based on CNN integrated machine learning

DOI：

英文关键词:classification of metal defects CNN ensemble learning machine learning mathematical integration models

基金项目:四川省市场监督管理局科技计划项目(SCSJZ2022007)资助

作者	单位
刘铭璇	1. 西南石油大学机电工程学院石油天然气装备教育部重点实验室
唐东林	1. 西南石油大学机电工程学院石油天然气装备教育部重点实验室
何媛媛	2. 四川省特种设备检验研究院
周立	1. 西南石油大学机电工程学院石油天然气装备教育部重点实验室

Author	Institution
Liu Mingxuan	1. School of Mechanical Engineering, Southwest Petroleum University
Tang Donglin	1. School of Mechanical Engineering, Southwest Petroleum University
He Yuanyuan	2. Sichuan Special Equipment Inspection Institute
Zhou Li	1. School of Mechanical Engineering, Southwest Petroleum University

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中文摘要:

针对金属缺陷分类,以深度学习为代表的分类方法主要是基于大规模数据的统计学习方法,一方面需要大量优质的标注样本,另一方面对数据中未能涵盖的样本泛化性能差。提出了一种利用集成学习思想,将人类分类知识嵌入到深度学习的少样本分类方法。首先搭建了一个卷积神经网络作为分类模型的骨干网络,并设计了一个利用机器学习的类人学习模块,利用人类分类所用特征进行分类。此外,为了提高模型的泛化性、鲁棒性和更好的融合效果,设计了一种以对数函数为核心的数学集成模型,模块中的数学集成模型利用集成学习思想将骨干网络和类人学习模块的输出进行耦合。实验结果表明,对于小训练集大测试集的金属缺陷数据在分类性能和训练参数量方面优于深度学习方法。此外,类人学习模块和数学集成模型嵌入到不同的骨干网络上均取得了很好的性能,表明所提出的方法适用于多种深度卷积神经网络。

英文摘要:

For the classification of metal defects, the mainstream classification methods represented by deep learning are mainly statistical learning methods based on large datasets. However, when applying deep learning, not only many quality labeled samples are needed, but also the result may suffer poor generalization. A classification approach with few samples is proposed, which embeds the hierarchical and concise knowledge of humanoid into deep learning. First, a CNN is built as the backbone of the classification model, and a humanoid learning module is designed, which uses the features of human classification to classify. To improve the generalization, robustness and better fusion effect of the model, a mathematical integration model based on logarithmic function is designed. The mathematical integration model in the module couples the outputs of backbone network and humanoid learning module by using the idea of integrated learning. The experimental results show that for the metal defect data of small training set and large test set, the classification performance and the amount of training parameters are better than the deep learning method. Humanoid learning module and mathematical integration model are embedded in different backbone, and good performance is achieved, which shows that the proposed method is suitable for various deep convolution neural networks.

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