Steel surface defect detection using convolutional neural network

Abstract

Steel is the most important engineering and construction material in the world. It is used in all aspects of our lives. But as every metal is can be defected and then will not be useful by the consumer Steel surface inspection has seen an important attention in relation with industrial quality of products. In addition, it has been studied in different methods based on image classification in the most of time, but these can detect only such kind of defects in very limited conditions such as illumination, obvious contours, contrast and noise...etc. In this paper, we aim to try a new method to detect steel defects this last depend on artificial intelligence and artificial neural networks. We will discuss the automatic detection of steel surface defects using the convolutional neural network, which can classify the images in their specific classes. The steel we are going to use will be well-classified weather the conditions of imaging are not the same, and this is the advantage of the convolutional neural network in our work. The accuracy and the robustness of the results are so satisfying