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

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


  • Guma Ali Department of Computer Science and Electrical Engineering, Muni University, Arua, Uganda
  • Emre Sadıkoğlu Department of Computer Engineering, Yalova University, Yalova, Turkey
  • Hatim Abdelhak Dida University of belhadj bouchaib ain temouchant,Algeria



Hybrid Approach


The automatic system for classifying traffic signs is a critical task of Advanced Driver Assistance Systems (ADAS) and a fundamental technique utilized as an integral part of the various vehicles. The recognizable features of a traffic image are utilized for their classification. Traffic signs are designed to contain specific shapes and colours, with some text and some symbols with high contrast to the background. This paper proposes a hybrid approach for classifying traffic signs by SIFT for image feature extraction and SVM for training and classification. The proposed work is divided into phases: pre-processing, Feature Extraction, Training, and Classification. MATLAB is used for the implementation purpose of the proposed framework, and classification is carried out by utilizing real traffic sign images


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How to Cite

Ali , 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.