MACHINE LEARNING TECHNIQUE TO DETECT DEFECTS ON THE STEEL SURFACE

Abstract

In this paper is presented a technique concerning the machine learning using computer vision data for the detection of defects on the steel surface. The early detection of defects can reduce the product damage and manufacturing cost. Moreover, it provides information that will provide data concerning quality of products and correct classification and this means that the product will not be rejected by the customer and avoid other costs. The non-contact inspection of the surface defects using the computer vision has become very popular in manufacturing industrial systems as it provides reliable and fast results. The machine learning achieved impressive results in image classification tasks, though it requires the previous learning phase to be completed. The proposed machine learning technique allows fast identification of defects and also permits learning during the process.