Detection and Classification of Traffic Signs with Deep Morphological Networks

The developments of computer aided driving systems detection and classification of traffic signs getting moreimportant everyday. In this subject there has been many different methods proposed and used. The morphological features of trafficsigns carries a lot of significance in terms of classificatio...

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Bibliographic Details
Published in2021 Innovations in Intelligent Systems and Applications Conference (ASYU) pp. 1 - 6
Main Authors Zerey, Furkan, Samil Fidan, Muhammet, Uslu, Erkan
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.10.2021
Subjects
classification
convolutional network
deep morphological network
detection
Integrated circuit modeling
Kernel
Lattices
Neural networks
Reactive power
Technological innovation
Tensors
traffic signs
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DOI10.1109/ASYU52992.2021.9599039

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Summary:The developments of computer aided driving systems detection and classification of traffic signs getting moreimportant everyday. In this subject there has been many different methods proposed and used. The morphological features of trafficsigns carries a lot of significance in terms of classification. The main idea of this study is using morphological features in deep learning network to enhance classification success rate. In this study the use of deep morphological networks is proposed for the sake of better utilizing morphological features in traffic sign efficiently.The results of deep morphological networks and convolutional neural networks are compared and deep morphological networks shows higher classification success rate.
DOI:10.1109/ASYU52992.2021.9599039