Surface Defects Classification Using Transfer Learning and Deep Sparse Coding
作者单位：College of Arts and ScienceNational University of Defense Technology College of Basic Military EducationNational University of Defense Technology
会议名称：《第40届中国控制会议》
会议日期：2021年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Surface Defects Classification Transfer Learning Sparse Coding
摘 要：Defect classification is an important part of automated surface defect detection ***,the types of surface defects of industrial products are complex and diverse,and the correct defect type classification can help to subsequently extract the characteristic of ***,defect classification can help to count the number of defect modes automatically and provide data support for the precise maintenance of product *** to the large number of surface defects and the small difference between defect types,traditional classification methods are difficult to classify defect ***,in order to improve the accuracy of defect image classification,this paper proposes a defect image classification method based on transfer learning and sparse ***,a deep CNN feature extraction algorithm for defect images is proposed in combination with transfer ***,the deep CNN features of the defect image are dimension-reduced and sparsely optimized using the sparse coding techniques,and the sparse CNN features are ***,the sparse CNN features are classified to realize the defect type determination using the linear *** accuracy of the proposed method is verified by using a steel surface defect image benchmark database,and the effectiveness of the proposed method is proved.