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.

中文翻译：

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通过特征选择和多类分类器相结合的带钢表面检测

目的

在钢带生产过程中，表面可能会出现一些缺陷，即传统的人工检测已不能满足低成本、高效率生产的要求。本文的目的是提出一种基于过滤方法结合隐贝叶斯分类器的特征选择方法，以提高缺陷识别的效率，降低计算的复杂度。该方法可以选择最优的混合模型，实现钢带表面缺陷的准确分类。

设计/方法/方法

基于离散小波变换特征提取方法初步获得了大图像特征集。然后使用三种特征选择方法（包括基于相关性的特征选择、一致性子集评估器 [CSE] 和信息增益）来优化特征空间。特征选择方法的参数基于隐藏朴素贝叶斯 (HNB) 算法的分类精度结果。然后将选定的特征子集应用于传统的 NB 分类器和领先的扩展 NB 分类器。

发现

实验结果表明，结合特征选择方法的HNB模型比其他缺陷识别模型具有更好的分类性能。在本研究的结果中，所提出的 CSE + HNB 混合模型在识别表面缺陷数据库的最佳子集方面是最稳健、最有效和最高分类精度的。

原创性/价值

本文的主要贡献是开发了一种结合特征选择和多类分类算法的混合模型，用于钢带表面检测。所提出的混合模型对于钢带表面检测主要是稳健和有效的。