Classification algorithms of strip surface defects based on PCA-ELM

The computer image technology has achieved impressive recognition rates in image classification tasks recently. In order to tackle the complex defect features in strip steel, we conduct a study of the effectiveness of principal component analysis and extreme learning machine (ELM) in the classification of strip surface in this paper. Using the principal component analysis, it extracts the characteristics of dimension reduction, obtains the characteristic quantity, reduces the dimension used as the input sample for the Extreme Learning Machine and trains the support vector machine and realizes the recognition. Compared with traditional extreme learning machine and support vector machine (SVM), PCA-ELM algorithm shows a higher accuracy and better generalization ability, which play a guiding sole on actual production.