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

Acta Technica Jaurinensis

Balázs Szűcs¹  Áron Ballagi² 

[1] Audi Hungaria Zrt, Product Unit Diesel I4/V6, Audi Hungária út 1, H-9027 Győr, Hungary;Széchenyi István University, Department of Automation, Egyetem tér 1, H-9026 Győr, Hungary;

关键词: machine learning;    classification;    convolutional neural network;    machine vision;    industry 4.0;   

DOI  :  10.14513/actatechjaur.v12.n4.511

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown