Intelligent Transport Systems, such as self-driving automobiles, represent a prime example of applications utilizing traffic sign recognition technology. The increasing demand for autonomous vehicles highlights a significant challenge: their ability to adhere to traffic regulations without human intervention for road safety. As technological advancements pave the way for increased automation, this study addresses the detection and identification of traffic sign boards under varying conditions, including lighting, direction, and diverse weather scenarios. Focusing on scenarios where the traffic sign is the sole object in an image with a minimal background, image processing techniques are employed for traffic sign recognition. Furthermore, the identified and classified sign is associated with its specific geospatial coordinates, using the device's location data, and subsequently stored in a database. This enables tracking of updates or changes to traffic signs along a designated route or area, with original information being replaced or overwritten upon any alteration at a particular location.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Driving with Deep Learning: A Robust Traffic Sign Detection and Classification System


    Beteiligte:


    Erscheinungsdatum :

    2024-01-04


    Format / Umfang :

    627483 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Traffic Sign Classification using Deep Learning

    Pothineni, Ramya Sree / Inampudi, Srinivas / Gudavalli, Lakshmi Yesaswini et al. | IEEE | 2023


    Traffic Sign Detection and Recognition using Deep Learning

    Oza, Rudri Mahesh / Geisen, Angelina / Wang, Taehyung | IEEE | 2021


    Fast and robust traffic sign detection

    Soetedjo, A. / Yamada, K. | Tema Archiv | 2005


    Traffic Sign Detection and Recognition Using Deep Learning Approach

    Rahman, Umma Saima / Maruf | Springer Verlag | 2023