Original scientific paper

A surface defect detection method of the magnesium alloy sheet based on deformable convolution neural network

S. Y. Guan ; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China
W. Y. Zhang ; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China
Y. F. Jiang ; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China

Abstract

In the rolling process of the magnesium alloy sheet, due to improper control parameters or inaccurate production equipment and other reasons, the surface of the magnesium alloy sheet is prone to appearance of edge crack, fold, inclusion, ripple, scratch and other defects. In order to improve the surface quality of the magnesium alloy sheet, a surface defect detection method based on deformable convolution neural network is proposed in the paper, which presents a higher detection accuracy than those traditional methods on the convolutional neural network (CNN), support vector machine (SVM) and Bayes. The experiment result shows the final detecting accuracy is greater than 95 %.

Keywords

magnesium alloy; sheet; surface quality; defects; deformable convolution neural network

Hrčak ID:

237029

URI

https://hrcak.srce.hr/237029

Publication date:

1.7.2020.

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