Autonomous robots, particularly in the field of delivery, play a crucial role in enhancing the safety and efficiency of intelligent transportation systems. This study introduces a delivery robot designed to recognise traffic signs, distinguishing them from other types of signs, to navigate autonomously and safely in urban environments. To achieve this, Convolutional Neural Networks (CNN) were employed for traffic sign classification. A diverse image dataset was used for training and evaluation. The model achieved an accuracy of $93.35 \%$, a precision of $\mathbf{9 4 . 0 1 \%}$, a recall of $\mathbf{9 2 . 6 0 \%}$, and an F1-score of $\mathbf{9 3 . 2 9 \%}$, demonstrating its strong ability to differentiate traffic signs from other objects. The confusion matrix analysis highlights the need to refine the handling of false positives and false negatives to enhance system reliability. These findings validate the effectiveness of CNNs in traffic sign recognition and emphasize the significance of expanding and diversifying training data to improve the model’s adaptability across different environments.


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    Title :

    Implementation of a Traffic Sign Recognition System Based on CNN for Autonomous Robots


    Contributors:


    Publication date :

    2025-07-01


    Size :

    1416482 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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