In urban areas, traffic signs are currently crucial for guiding drivers safely. Traffic signs utilizes YOLOv8, an advanced object detection algorithm, to enhance the performance of autonomous driving systems in detecting speed limit signs. The aim is to solve the problem of self-driving cars misreading speed limit signs. The paper highlights YOLOv8's capabilities in accurately and adaptively detecting speed limit signs and discusses its integration into autonomous driving systems for improved decision-making. Additionally, the potential application of YOLOv8 in smart traffic management and Advanced Driver Assistance Systems (ADAS) is explored, with an emphasis on traffic safety enhancement through effective speed limit sign recognition. The dataset, sourced from the Roboflow platform, comprises 10,000 images featuring 29 distinct classes of signboards. The implemented model demonstrates exceptional performance, achieving an impressive 80% accuracy and a mean Average Precision (mAP) of 0.93. Enhancing navigation and safety, the model can be augmented with ADAS.


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    Title :

    Traffic Sign-Based Speed Control for Autonomous Driving Systems Using YOLOv8


    Contributors:


    Publication date :

    2024-05-24


    Size :

    1941975 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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