摘要
针对金属缺陷识别分类,传统机器学习需要人工提取特征,而深度学习需要大量样本的问题,本文针对中小规模缺陷数据集提出了一种基于浅层的卷积神经网络(CNN)和决策树(DT)的金属缺陷分类方法。利用卷积神经网络提取特征,通过决策树分类,实现缺陷分类。引入主成分分析(PCA)方法对特征向量降维,减小过拟合并提升算法识别分类效率。为验证本文方法的通用性,除图像缺陷数据外还引入非图像缺陷数据。实验结果表明,本文方法除了能分类图像缺陷也能分类非图像缺陷,且在识别率等3个评价指标上本文方法优于传统机器学习方法,与深度学习方法持平,但在分类消耗时间上少于深度学习。
For recognition and classificationof metal defect,traditional machine learning requires manual feature extraction while deep learning requires a large number of samples.This paper proposes a classification method of metal defectbased on the shallow convolutional neural network(CNN)and decision tree(DT)for small and medium-sized defect data sets.The feature is extracted by the convolutional neural network,and the defect is classified with the decision tree.The principal component analysis(PCA)method is introduced to reduce the dimension of feature vectors to reduce the efficiency of recognition and classification by overfitting.In order to verify the generality of this method,non-image defect data is introduced in addition to image defect data.Experimental results show that the present method can classify not only image defects but also non-image defects,and is superior to traditional machine learning methods in three evaluation indexes of recognition rate,equal to deep learning methods,but less timeconsuming than deep learning in classification.
作者
唐东林
周立
吴续龙
宋一言
秦北轩
TANG Donglin;ZHOU Li;WU Xulong;SONG Yiyan;QIN Beixuan(School of Mechanical Engineering,Southwest Petroleum University,Chengdu 610500,China)
出处
《机械科学与技术》
CSCD
北大核心
2022年第9期1420-1427,共8页
Mechanical Science and Technology for Aerospace Engineering
基金
四川省科技支撑项目(2017FZ0033)
成都市技术创新研发项目(2018-YF05-00201-GX)
西南石油大学国家重点实验室项目(PLN201828)。
关键词
金属缺陷识别分类
卷积神经网络
决策树
主成分分析
classification of metal defects
convolutional neural network
decision tree
principal component analysis
