Accuracy Improvement by Artificial Neural Networks in Technical Vision System

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

  • Gabriel Trujillo-Hernández
  • Julio C. Rodríguez-Quiñonez
  • Luis R. Ramírez-Hernández
  • Moises J. Castro-Toscano
  • Daniel Hernández-Balbuena
  • Wendy Flores-Fuentes
  • Oleg Sergiyenko
  • Lars Lindner
  • Paolo Mercorelli
This paper proposes an Artificial Neural Network (ANN) to accurately predict the real angles obtained by a Triangulation Vision System. The performance of the ANN is compared with the K-Nearest Neighbors algorithm from previous publications. For the experimentation it was necessary to create a database to train and prove both methods in different coordinates on a determinate area through the dynamic triangulation method. Afterwards, the root mean square error is calculated to obtain the accuracy of each algorithm. Finally, several laser scanning measurements were taken at different distances to analyze the measurement dispersion of both algorithms.
OriginalspracheEnglisch
TitelIECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
Anzahl der Seiten6
ErscheinungsortPiscataway
VerlagIEEE - Institute of Electrical and Electronics Engineers Inc.
Erscheinungsdatum10.2019
Seiten5572-5577
Aufsatznummer8927596
ISBN (Print)978-1-7281-4879-3
ISBN (elektronisch)978-1-7281-4878-6
DOIs
PublikationsstatusErschienen - 10.2019
Veranstaltung45th Annual Conference of the Institute of Electrical and Electronics Engineers' Industrial Electronics Society - 2019 - Lisbon Congress Center, Lisbon, Portugal
Dauer: 14.10.201917.10.2019
Konferenznummer: 45
https://iecon2019.org/

DOI

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Forschende

  1. Mathis Brinkmann

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