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Steel strip surface inspection through the combination of feature selection and multiclass classifiers

Z.F. Zhang (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China)
Wei Liu (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China)
Egon Ostrosi (Université de Bourgogne Franche-Comté, UTBM, Belfort, France)
Yongjie Tian (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China)
Jianping Yi (Shanghai Customs, Shanghai, China)

Engineering Computations

ISSN: 0264-4401

Article publication date: 23 September 2020

Issue publication date: 17 June 2021

201

Abstract

Purpose

During the production process of steel strip, some defects may appear on the surface, that is, traditional manual inspection could not meet the requirements of low-cost and high-efficiency production. The purpose of this paper is to propose a method of feature selection based on filter methods combined with hidden Bayesian classifier for improving the efficiency of defect recognition and reduce the complexity of calculation. The method can select the optimal hybrid model for realizing the accurate classification of steel strip surface defects.

Design/methodology/approach

A large image feature set was initially obtained based on the discrete wavelet transform feature extraction method. Three feature selection methods (including correlation-based feature selection, consistency subset evaluator [CSE] and information gain) were then used to optimize the feature space. Parameters for the feature selection methods were based on the classification accuracy results of hidden Naive Bayes (HNB) algorithm. The selected feature subset was then applied to the traditional NB classifier and leading extended NB classifiers.

Findings

The experimental results demonstrated that the HNB model combined with feature selection approaches has better classification performance than other models of defect recognition. Among the results of this study, the proposed hybrid model of CSE + HNB is the most robust and effective and of highest classification accuracy in identifying the optimal subset of the surface defect database.

Originality/value

The main contribution of this paper is the development of a hybrid model combining feature selection and multi-class classification algorithms for steel strip surface inspection. The proposed hybrid model is primarily robust and effective for steel strip surface inspection.

Keywords

Acknowledgements

This work was supported by the National Key Research and Development Program of China (Grant Number 2018YFC0809203) and the National Natural Science Foundation of China (Grant Number 51205242). The authors express sincere appreciation to the anonymous referees for their helpful comments.

Citation

Zhang, Z.F., Liu, W., Ostrosi, E., Tian, Y. and Yi, J. (2021), "Steel strip surface inspection through the combination of feature selection and multiclass classifiers", Engineering Computations, Vol. 38 No. 4, pp. 1831-1850. https://doi.org/10.1108/EC-11-2019-0502

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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