The development of automatic detection and recognition of traffic signs is essential for intelligent transportation solutions (ITS) and the integration with autonomous systems. The proposed system integrates traffic sign data with a Raspberry Pi platform and Deep Learning methods to detect and recognize various traffic signs. It addresses the challenges associated with detecting and recognizing traffic signs, enhancing the safety and efficiency of ITS systems. The system receives real-time input from cameras positioned at traffic signals, which is processed using YOLOv5. Following processing, the output is sent to a Raspberry Pi via WebSocket and asynchronous I/O Python libraries. Using Blynk-based technology, the Raspberry Pi then carries out the appropriate steps, such changing the speed of the vehicle or halting.


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    Titel :

    AI enhanced Autonomous Traffic Sign Recognition and Action System


    Beteiligte:
    Dhivyapraba, R. (Autor:in) / Kavitha, K. (Autor:in) / P, Karthik (Autor:in) / P, Kaushik (Autor:in)


    Erscheinungsdatum :

    04.04.2025


    Format / Umfang :

    887692 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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