Conference Article

·2021

Detection and Classification of Traffic Signs with Deep Morphological Networks

Furkan Zerey YTU , Muhammet Samil Fidan YTU , Erkan Uslu YTU

2021 Innovations in Intelligent Systems and Applications Conference (ASYU)

Abstract

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.

Keywords

Convolutional neural network Computer science Deep learning Artificial intelligence Artificial neural network Pattern recognition (psychology) Machine learning

Subject Areas

Vehicle License Plate Recognition ·Media Technology ·Physical Sciences
Video Surveillance and Tracking Methods ·Computer Vision and Pattern Recognition ·Physical Sciences
Currency Recognition and Detection ·Computer Vision and Pattern Recognition ·Physical Sciences

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