Strip Surface Defects Recognition Based on PSO-RS&amp;SOCP-SVM Algorithm

Dongyan Cui,Kewen Xia

doi:10.1155/2017/4257273

Abstract

In order to improve the strip surface defect recognition and classification accuracy and efficiency, Rough Set (RS) attribute reduction algorithm based on Particle Swarm Optimization (PSO) algorithm was used on the optimal selection of strip surface defect image decision features, which removed redundant attributes, provided reduction data for the follow-up Support Vector Machine (SVM) model, reduced vector machine learning time, and constructed the SVM classifier, which uses Second-Order Cone Programming (SOCP) and multikernel Support Vector Machine classification model. Six kinds of typical defects such as rust, scratch, orange peel, bubble, surface crack, and rolled-in scale are recognized and classification is made using this classifier. The experimental results show that the classification accuracy of the proposed algorithm is 99.5%, which is higher than that of SVM algorithm and Relevance Vector Machine (RVM) algorithm. And because of using the Rough Set attribute reduction algorithm based on PSO algorithm, the learning time of SVM is reduced, and the average time of the classification and recognition model is 58.3 ms. In summary, the PSO-RS&SOCP-SVM evaluation model is not only more efficient in time, but also more worthy of popularization and application in the accuracy.

Highlights

Strip surface defect recognition is a kind of pattern recognition problem with multifeatures and multitypes, which is quite complicated
We study the decision feature selection and classification of the cold rolled strip shape defect images based on Rough Set theory and Support Vector Machine
We study the attribute reduction algorithm based on evolutionary computation used to deal with the high dimension decision table

Summary

Introduction

Strip surface defect recognition is a kind of pattern recognition problem with multifeatures and multitypes, which is quite complicated. Genetic Algorithm is an efficient optimization method, but its complex genetic operation makes it difficult to meet the expectations in convergence speed and accuracy These algorithms cannot take into account the validity of the defect characteristics and reduce the complexity of the computation time when the features of the surface defect of steel are optimized. In [2, 3], the Relief F algorithm does the corresponding improvement and supplement In view of this situation, it is necessary to further study the new feature selection and dimension reduction method, so as to meet the needs of cold strip steel surface defect detection. We study the decision feature selection and classification of the cold rolled strip shape defect images based on Rough Set theory and Support Vector Machine. The proposed model is used to classify and identify the surface defect images of the steel strip, and the effectiveness of the proposed method is verified

Rough Set Attribute Reduction Based on PSO Algorithm

Description of the Attribute Reduction Method Based on PSO Algorithm

Experiment Simulation and Result Analysis

Conclusions