Deep learning in Transportation: Optimized driven deep residual networks for Arabic traffic sign recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50020679" target="_blank" >RIV/62690094:18470/23:50020679 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1110016823007329?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1110016823007329?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.aej.2023.08.047" target="_blank" >10.1016/j.aej.2023.08.047</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep learning in Transportation: Optimized driven deep residual networks for Arabic traffic sign recognition

Popis výsledku v původním jazyce

Car manufacturers around the globe are in a race to design and build driverless cars. The concept of driverless is also being applied to any moving vehicle such as wheelchairs, golf cars, tourism carts in recreational parks, etc. To achieve this ambition, vehicles must be able to drive safely on streets stay within required lanes, sense moving objects, sense obstacles, and be able to read traffic signs that are permanent and even temporary signs. It will be a completely integrated system of the Internet of Things (IoT), Global Positioning System (GPS), Machine Learning (ML)/Deep Learning (DL), and Smart Technologies. A lot of work has been done on traffic sign recognition in the English language, but little has been done for Arabic traffic sign recognition. The concepts used for traffic sign recognition can also be applied to indoor signage, smart cities, supermarket labels, and others. In this paper, we propose two optimized Residual Network (ResNet) models (ResNet V1 and ResNet V2) for automatic traffic sign recognition using the Arabic Traffic Signs (ArTS) dataset. Additionally, the authors developed a new dataset specifically for Arabic Traffic Sign recognition consisting of 2,718 images taken from random places in the Eastern province of Saudi Arabia. The optimized proposed ResNet V1 model achieved the highest training and validation accuracies of 99.18% and 96.14%, respectively. It should be noted here that the authors accounted for both overfitting and underfitting in the proposed models. It is also important to note that the results achieved using the proposed models outperform similar methods proposed in the extant literature for the same dataset or similar-size dataset.

Název v anglickém jazyce

Deep learning in Transportation: Optimized driven deep residual networks for Arabic traffic sign recognition

Popis výsledku anglicky

Car manufacturers around the globe are in a race to design and build driverless cars. The concept of driverless is also being applied to any moving vehicle such as wheelchairs, golf cars, tourism carts in recreational parks, etc. To achieve this ambition, vehicles must be able to drive safely on streets stay within required lanes, sense moving objects, sense obstacles, and be able to read traffic signs that are permanent and even temporary signs. It will be a completely integrated system of the Internet of Things (IoT), Global Positioning System (GPS), Machine Learning (ML)/Deep Learning (DL), and Smart Technologies. A lot of work has been done on traffic sign recognition in the English language, but little has been done for Arabic traffic sign recognition. The concepts used for traffic sign recognition can also be applied to indoor signage, smart cities, supermarket labels, and others. In this paper, we propose two optimized Residual Network (ResNet) models (ResNet V1 and ResNet V2) for automatic traffic sign recognition using the Arabic Traffic Signs (ArTS) dataset. Additionally, the authors developed a new dataset specifically for Arabic Traffic Sign recognition consisting of 2,718 images taken from random places in the Eastern province of Saudi Arabia. The optimized proposed ResNet V1 model achieved the highest training and validation accuracies of 99.18% and 96.14%, respectively. It should be noted here that the authors accounted for both overfitting and underfitting in the proposed models. It is also important to note that the results achieved using the proposed models outperform similar methods proposed in the extant literature for the same dataset or similar-size dataset.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Alexandria Engineering Journal

ISSN

1110-0168

e-ISSN

2090-2670

Svazek periodika

80

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

134-143

Kód UT WoS článku

001065585500001

EID výsledku v databázi Scopus

2-s2.0-85169293395