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

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.

[1]  Theresa Beaubouef,et al.  Rough Sets , 2019, Lecture Notes in Computer Science.

[2]  Julio López,et al.  Imbalanced data classification using second-order cone programming support vector machines , 2014, Pattern Recognit..

[3]  Sungyoung Lee,et al.  Rough set-based approaches for discretization: a compact review , 2015, Artificial Intelligence Review.

[4]  Junichi Hori,et al.  Classification of silent speech using support vector machine and relevance vector machine , 2014, Appl. Soft Comput..

[5]  Fumin Ma,et al.  Algorithm for attribute relative reduction based on generalized binary discernibility matrix , 2014, The 26th Chinese Control and Decision Conference (2014 CCDC).

[6]  Qingxin Zhu,et al.  Matroidal structure of rough sets and its characterization to attribute reduction , 2012, Knowl. Based Syst..

[7]  廖士中,et al.  KMA-α:A Kernel Matrix Approximation Algorithm for Support Vector Machines , 2012 .

[8]  R.P.L. Durgabai,et al.  Feature Selection using ReliefF Algorithm , 2014 .

[9]  K. Thanushkodi,et al.  A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization , 2010, ArXiv.

[10]  Feng Hu,et al.  A fast algorithm for attribute reduction based on Trie tree and rough set theory , 2013, Other Conferences.

[11]  Ying Liu,et al.  Rough set and PSO-based ANFIS approaches to modeling customer satisfaction for affective product design , 2015, Adv. Eng. Informatics.

[12]  Dong Yao,et al.  An Approach to Attribute Reduction Based on Attribute Similarity , 2005 .

[13]  Guo Fu Yin,et al.  Image Classification for Steel Strip Surface Defects Based on Support Vector Machines , 2011 .

[14]  Han Ying-l.  Multidimensional mixing features extraction and recognition for surface defect of steel strip , 2015 .

[15]  Sebastián Ventura,et al.  ReliefF-ML: An Extension of ReliefF Algorithm to Multi-label Learning , 2013, CIARP.

[16]  Srilatha Chebrolu,et al.  Attribute Reduction in Decision-Theoretic Rough Set Model using Particle Swarm Optimization with the Threshold Parameters Determined using LMS Training Rule☆ , 2015 .

[17]  Leandro dos Santos Coelho,et al.  Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).