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Traffic Sign Recognition with Neural Networks in the Frequency Domain

  • In this paper we describe traffic sign recognition with neural networks in the frequency domain. Traffic signs exist in all countries to regulate the traffic of vehicles and pedestrians. Each country has its own set of traffic signs that are more or less similar. They consist of a set of abstract forms, symbols, numbers and letters, which are combined into different signs. Automatic traffic sign recognition is important for driver assistance systems and for autonomous driving. Traffic sign recognition is a subtype of image recognition. The traffic signs are usually recorded by a camera and must be recognized in real time, i.e. assigned to a class. We use neural networks for traffic sign recognition. The special feature of our method is that the traffic sign recognition does not take place in the spatial domain but in the frequency domain. This has advantages because it is possible to significantly reduce the number of neurons and thus the computing effort of the neural network compared to a conventional neural network.

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Metadaten
Verfasserangaben:Florian Franzen, Chunrong Yuan, Zhong Li
URN:urn:nbn:de:hbz:832-epub4-19800
DOI:https://doi.org/10.1088/1742-6596/1576/1/012015
ISSN:1742-6588
ISSN:1742-6596
Titel des übergeordneten Werkes (Englisch):Journal of Physics: Conference Series
Verlag:IOP Publishing
Dokumentart:Wissenschaftlicher Artikel
Sprache:Englisch
Datum der Veröffentlichung:01.06.2020
Datum der Freigabe:10.05.2022
Jahrgang:1576
Ausgabe / Heft:1
Aufsatznummer:012015
Seitenzahl:6
Fakultäten und Zentrale Einrichtungen:Informations-, Medien- und Elektrotechnik (F07) / Fakultät 07 / Institut für Nachrichtentechnik
DDC-Sachgruppen:600 Technik, Medizin, angewandte Wissenschaften
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International