Reducing Pseudo-error Rate of Industrial Machine Vision Systems with Machine Learning Methods

  • Balázs Szűcs Audi Hungaria Zrt
  • Áron Ballagi Széchenyi István University, Automation Department
Keywords: machine learning, classification, convolutional neural network, machine vision, industry 4.0

Abstract

Nowadays machine learning and artificial neural networks are hot topic. These methods gains more and more ground in everyday life. In addition to everyday usage, an increasing emphasis placed on industrial use. In the field of research and development, materials science, robotics and thanks to the spread of Industry 4.0 and digitalization, more and more machine learning based systems introduced in production. This paper gives examples of possible ways of using machine learning algorithms in manufacturing, as well as reducing pseudo-error (false positive) rate of machine vision quality control systems. Even the simplest algorithms and models can be very effective on real-world problems. With the usage of convolutional neural networks, the pseudo-error rate of the examined system reducible.

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Published
2019-10-18
How to Cite
Szűcs, B. and Ballagi, Áron (2019) “Reducing Pseudo-error Rate of Industrial Machine Vision Systems with Machine Learning Methods”, Acta Technica Jaurinensis, 12(4), pp. pp. 294-305. doi: 10.14513/actatechjaur.v12.n4.511.
Section
Information Technology and Electrical Engineering