Design a Hybrid Approach for the Classification and Recognition of Traffic Signs Using Machine Learning

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dc.contributor.authorGuma, Ali
dc.contributor.authorSadıkoğlu, Emre
dc.contributor.authorAbdelhak, Hatim
dc.date.accessioned2023-07-17T15:19:59Z
dc.date.available2023-07-17T15:19:59Z
dc.date.issued2023
dc.description.abstractAdvanced Driver Assistance Systems (ADAS) are a fundamental part of various vehicles, and the automatic classification of traffic signs is a crucial component. A traffic image is classified based on its recognizable features. Traffic signs are designed with specific shapes and colours, along with text and symbols that are highly contrasted with their surroundings. This paper proposes a hybrid approach for classifying traffic signs by combining SIFT with SVM for training and classification. There are four phases to the proposed work: pre-processing, feature extraction, training, and classification. A real traffic sign image is used for classification in the proposed framework, and MATLAB is used to implement the frameworken_US
dc.identifier.citationAli, G., Sadıkoğlu, E., & Abdelhak, H. (2023). Design a Hybrid Approach for the Classification and Recognition of Traffic Signs Using Machine Learning. Wasit Journal of Computer and Mathematics Science, 2(2), 18-25. https://doi.org/10.31185/wjcm.151en_US
dc.identifier.urihttps://doi.org/10.31185/wjcm.151
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/9068
dc.language.isoenen_US
dc.publisherWasit Journal of Computer and Mathematics Scienceen_US
dc.subjectClassificationen_US
dc.subjectMachine Learningen_US
dc.subjectDriver Assistance Systemen_US
dc.titleDesign a Hybrid Approach for the Classification and Recognition of Traffic Signs Using Machine Learningen_US
dc.typeArticleen_US
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