This paper presents the development and evaluation of the Real-Time Traffic Sign Detection Model (RTTSDM), lever-aging the YOLOv4 framework with a CSPDarknet53 backbone, trained on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. The RTTSDM exhibits significant advancements in traffic sign detection, showing substantial improvements across key performance metrics including average Intersection over Union (IoU), precision, recall, F1 Score, and mean Average Precision (mAP) throughout training. Notably, the model achieves a peak mAP of 0.9685 at iteration 4000, reflecting its superior ability to accurately identify and classify traffic signs. The model demonstrates robust performance under various lighting conditions, effectively detecting prohibitory, danger, mandatory, and priority signs with high accuracy. The RTTSDM shows strong potential to improve road safety and traffic management through its precise and reliable traffic sign detection capabilities.


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    Title :

    A Reliable Real-Time Traffic Sign Detection Model for Diverse Environmental Challenges


    Contributors:


    Publication date :

    2024-12-06


    Size :

    669737 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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