In today’s life, Traffic sign identification is a significant domain of environment awareness system. This traffic sign identification is becoming a top priority for modern transportation systems as it is highly essential to maintain the road safety nowadays. While detecting the traffic signs using various target detection techniques, many real-time problems are being faced like easy omission, undesirable light, inaccurate positioning for traffic signs (during detection), disorientation, motion blur, color fade, occlusion, rain, and snow. In view of these problems that the traffic signs cannot be recognized well, many novel target detection technologies are emerging, which in-turn solves these problems. This article introduces a reliable traffic sign categorization system, with the help of OpenCV for image enhancement and a five-layered Convolution Neural Network. The significance of sophisticated traffic sign identification for preventing accidents and promoting road safety is emphasized by this research that classifies traffic signs. The proposed CNN model has proved to achieve a remarkable classification accuracy and flexibility in response to changes in sign and environment, as demonstrated by the outcomes of the experiments. The strength of the proposed model has been tested on the German Traffic Sign Dataset and the experimental results have unfolded the fact that this model has recognized German traffic signs, with a better classification accuracy of 97.3%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Traffic Sign Classification for Road Safety using CNN


    Contributors:


    Publication date :

    2024-02-09


    Size :

    468621 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Road traffic sign detection and classification

    Escalera, A. de la / Moreno, L.E. / Salichs, M.A. et al. | Tema Archive | 1997


    Traffic Road Sign Detection and Classification

    Mehdi Fartaj / Sedigheh Ghofrani | DOAJ | 2024

    Free access

    Traffic safety system using Convertible road sign device

    European Patent Office | 2016

    Free access

    TRAFFIC SAFETY SYSTEM USING CONVERTIBLE ROAD SIGN DEVICE

    LEE JAE YONG | European Patent Office | 2016

    Free access

    VIRTUALIZED ROAD TRAFFIC SIGN GENERATION FOR ENHANCING ROAD SAFETY

    GIRMA MAMUYE YILMA / FRANCESCO DEVOTI / FAQIR ZARRAR YOUSAF | European Patent Office | 2022

    Free access