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Traffic-Sign Recognition

Author

Listed:
  • Marcel PRODAN

  • Gabriel DOROBANTU

  • Narcis IONITA

  • Mihai-Lucian VONCILA

  • Nicolae TARBA
  • Costin-Anton BOIANGIU

    (National University of Science and Technology POLITEHNICA Bucharest, Romania)

  • Nicolae GOGA

    (National University of Science and Technology POLITEHNICA Bucharest, Romania)

Abstract

Traffic-sign recognition is critical for vehicle safety applications, especially as self-driving cars become a reality. This paper proposes a solution based on existing approaches, utilizing deep learning and computer vision preprocessing to create a real-time algorithm that addresses the limitations of previous methods. The proposed algorithm aims to overcome as many drawbacks as possible and serve as a core component of advanced driver- assistance systems (ADAS). The proposed method is evaluated using the German Traffic Sign Recognition Benchmark (GTSRB) and the Belgium Traffic Sign Dataset (BTSD). This study concludes with a fully functional pipeline that can inspire the development of driving assistants and advance the future of self-driving cars.

Suggested Citation

  • Marcel PRODAN & Gabriel DOROBANTU & Narcis IONITA & Mihai-Lucian VONCILA & Nicolae TARBA & Costin-Anton BOIANGIU & Nicolae GOGA, 2024. "Traffic-Sign Recognition," Journal of Information Systems & Operations Management, Romanian-American University, vol. 18(1), pages 186-205, May.
    • Handle: RePEc:rau:jisomg:v:18:y:2024:i:1:p:186-205
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    File URL: http://www.rebe.rau.ro/RePEc/rau/jisomg/SU24/JISOM-SU24-A14.pdf
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