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2025 | OriginalPaper | Chapter

29. Training von ambienten Fahrzeugbeleuchtungen aus subjektiv bewerteten Bildern mit Deep Belief Networks unter Verwendung der Enforcing-Rule Supervised und eines cue validity factors

Authors : Gregor Braun, Michel Brokamp, Christina Klüver

Published in: Neue Algorithmen für praktische Probleme

Publisher: Springer Fachmedien Wiesbaden

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Zusammenfassung

Die Qualitätsprüfung von Umgebungsbeleuchtung in Fahrzeugen kann von der subjektiven Wahrnehmung des Prüfers hinsichtlich der Lichthomogenität beeinflusst werden. Infolgedessen entspricht auch die Klassifizierung von Trainingsdaten diesem subjektiven Eindruck. In diesem Beitrag wird gezeigt, wie Bilder von Lichtleitern von Fahrzeugumgebungsbeleuchtung verwendet werden, um Deep Belief Networks (DBN) unter Verwendung der Lernregeln „Backpropagation“ (BP) und „Enforcing Rule Supervised“ (ERS) zu trainieren. Darüber hinaus wird die Auswirkung der kontrastiven Divergenz (contrastive divergences) des Vortrainings auf die Robustheit und Genauigkeit der trainierten Netze sowie der Effekt eines cue validitiy factors (cvf) auf die Trainingsbilder analysiert. Die Ergebnisse sind vielversprechend und legen eine Entscheidungsunterstützung durch DBN im Produktionsprozess nahe, um den Einfluss der Subjektivität menschlicher Prüfer zu minimieren.

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Metadata
Title
Training von ambienten Fahrzeugbeleuchtungen aus subjektiv bewerteten Bildern mit Deep Belief Networks unter Verwendung der Enforcing-Rule Supervised und eines cue validity factors
Authors
Gregor Braun
Michel Brokamp
Christina Klüver
Copyright Year
2025
DOI
https://doi.org/10.1007/978-3-658-43319-2_29

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