A Lightweight Convolutional Neural Network (CNN) Architecture for Traffic Sign Recognition in Urban Road Networks

Recognizing and classifying traffic signs is a challenging task that can significantly improve road safety. Deep neural networks have achieved impressive results in various applications, including object identification and automatic recognition of traffic signs. These deep neural network-based traffic sign recognition systems may have limitations in practical applications due to their computational requirements and resource consumption. To address this issue, this paper presents a lightweight neural network for traffic sign recognition that achieves high accuracy and precision with fewer train... Mehr ...

Verfasser: Muneeb A. Khan
Heemin Park
Jinseok Chae
Dokumenttyp: Text
Erscheinungsdatum: 2023
Verlag/Hrsg.: Multidisciplinary Digital Publishing Institute
Schlagwörter: traffic sign recognition / intelligent transportation system / convolutional neural network / German traffic sign recognition benchmark dataset / Belgium traffic sign benchmark dataset
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-26583489
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://doi.org/10.3390/electronics12081802