An automatic system to surface defect classification of hot rolled steel

Abstract

The primary objective of this paper is to develop an automatic surface defect inspection system for hot-rolled flat steel The proposed technique consists of four major steps. The proposed technique consists of four major steps. The first step is image acquisition. The second step is features extraction of image by Histogram of oriented gradients (HOG). In the third step the principal component analysis (PCA) is applied on the HOG descriptor to reduce the dimensionality of the feature vector. In the final step the K- nearest neighbor classifier (KNN) is used to classify the different steel surface defects. The experimental results showed that the proposed steel inspection system which is based on KNN classifier provide a better results and recognition accuracy of 91.12%.