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Artificial intelligence-powered smart roads: leveraging orange3 for traffic signs recognition

IAES International Journal of Artificial Intelligence · Vol. 14 No. 5 (2025) · hlm. 3816–3826

DOI
10.11591/ijai.v14.i5.pp3816-3826
ISSN
2252-8938, 2089-4872
Terbit
2025-10-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Traffic sign recognition systems are an important concern of advance driver assistance systems (ADAS) and intelligent autonomous vehicles. Recently, many studies have emerged that aim to employ artificial intelligence (AI) and machine learning (ML) to detect and classify traffic signs to improve a system that can be embedded in vehicles to increase efficiency and safety. This work's primary goal is to address traffic sign identification and recognition utilizing a 2,339-image open-source dataset from Kaggle. Our detection model for extracting and classifying traffic sign suggestions is built using Orange3 data mining tools, based on four classifiers random forest (RF), k-nearest neighbors (KNN), decision tree (DT), and adaptive boosting (AdaBoost). Signs are classified into eight categories: don't go signs, go signs, horn signs, roundabout signs, danger signs, crossing signs, speed limit sign, and unallowed signs. The results of examining and evaluating the proposed model based on the performance evaluation metrics showed that RF outperformed with an accuracy rate of 99.8%, followed by AdaBoost with a classification accuracy of 99.2%, and the classification accuracy of DT and KNN was 98.3% and 94.9%, respectively.

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  • Al-Ahliyya Amman University
  • World Islamic Sciences and Education University
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Rekan penulisOrganisasiArtikel bersama
Areen M. ArabiatAl-Ahliyya Amman University4
Omar AlmomaniWorld Islamic Sciences and Education University3
Amani M. Al-GhraibahAl-Ahliyya Amman University2
Hamza Abu OwidaAfiliasi tidak diketahui2
Mohammad HassanAfiliasi tidak diketahui1
Amani Al-GhraibahAfiliasi tidak diketahui1
Hamza Abu OwidaAl-Ahliyya Amman University1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Artificial intelligence-powered smart roads: leveraging orange3 for traffic signs recognition</dc:title>
	<dc:creator>Arabiat, Areen</dc:creator>
	<dc:creator>Altayeb, Muneera</dc:creator>
	<dc:creator>Salama, Sanaa</dc:creator>
	<dc:subject xml:lang="en-US">Computer engineering ; Image processing ;Pattern Recognition ;Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">AdaBoost; Advance driver assistance systems; Performance metrices; Random forest; Traffic signs</dc:subject>
	<dc:description xml:lang="en-US">Traffic sign recognition systems are an important concern of advance driver assistance systems (ADAS) and intelligent autonomous vehicles. Recently, many studies have emerged that aim to employ artificial intelligence (AI) and machine learning (ML) to detect and classify traffic signs to improve a system that can be embedded in vehicles to increase efficiency and safety. This work's primary goal is to address traffic sign identification and recognition utilizing a 2,339-image open-source dataset from Kaggle. Our detection model for extracting and classifying traffic sign suggestions is built using Orange3 data mining tools, based on four classifiers random forest (RF), k-nearest neighbors (KNN), decision tree (DT), and adaptive boosting (AdaBoost). Signs are classified into eight categories: don't go signs, go signs, horn signs, roundabout signs, danger signs, crossing signs, speed limit sign, and unallowed signs. The results of examining and evaluating the proposed model based on the performance evaluation metrics showed that RF outperformed with an accuracy rate of 99.8%, followed by AdaBoost with a classification accuracy of 99.2%, and the classification accuracy of DT and KNN was 98.3% and 94.9%, respectively.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-10-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/27034</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i5.pp3816-3826</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 5: October 2025; 3816-3826</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i5</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/27034/14722</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Areen Arabiat, Muneera Altayeb, Sanaa Salama</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>