This study developed a traffic sign detection and recognition algorithm based on the RetinaNet. Two main aspects were revised to improve the detection of traffic signs: image cropping to address the issue of large image and small traffic signs and more anchors with various scales to detect traffic signs with different sizes and shapes. The proposed algorithm was trained and tested in a series of autonomous driving front-view images in a virtual simulation environment. Results show that the algorithm performed well under good illumination and weather conditions. The drawbacks are that it sometimes failed to detect objects under bad weather conditions like snow and failed to distinguish speed limit signs with different limit values.


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    Titel :

    Traffic Sign Detection and Recognition for Autonomous Driving in Virtual Simulation Environment


    Beteiligte:
    Zhu, Meixin (Autor:in) / Yang, Hao (Frank) (Autor:in) / Cui, Zhiyong (Autor:in) / Wang, Yinhai (Autor:in)

    Kongress:

    International Conference on Transportation and Development 2022 ; 2022 ; Seattle, Washington



    Erscheinungsdatum :

    31.08.2022




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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