An automatic system to surface defect classification of hot rolled steel
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Date
2018-12-10
Journal Title
Journal ISSN
Volume Title
Publisher
University of Eloued جامعة الوادي
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%.
Description
International Symposium on Mechatronics and Renewable Energies El-Oued 10- 11 December 2018
Keywords
Industrial vision, the K- nearest neighbor classifier, classification of surface defects, Histogram of Oriented Gradients (HOG), Principal Component Analysis (PCA)