Traffic sign detection and recognition are the two main factors in the development process of an intelligent transportation system and independent vehicles. This paper introduces a novel approach for real-time traffic sign detection and recognition, utilizing the You Only Look Once version 9 (YOLOv9) algorithm for enhanced efficiency. YOLOv9 is one of the deep learning models that are advanced and contribute to a great improvement in terms of accuracy and speed compared with its predecessors. The proposed model is especially designed to suit the diverse and complex traffic environment encountered in Indian cities. By availing a robust dataset of Indian traffic signs, the model detected and classified several sign types with a high level of precision. We have further explored the integration of this system with smart traffic management for better safety and flow of vehicles at an urban junction. Experimental results confirm that the proposed method achieves higher detection accuracy and faster processing speed compared to existing techniques. Therefore, the proposed YOLOv9-based method is capable of being used practically for real-world applications.


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    Title :

    Real-Time Traffic Sign Recognition for Smart Transportation in Indian Urban Environments Using YOLOv9


    Contributors:


    Publication date :

    2025-01-20


    Size :

    584832 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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