Automated cars are being developed by leading automakers and this technology is expected to revolutionize how people experience transportation, giving people more options and convenience. The implementation of traffic signal detection and recognition systems in automated vehicles can help reduce the number of fatalities due to traffic mishaps, improve road safety and efficiency, decrease traffic congestion, and help reduce air pollution. Once the traffic signs and lights are detected, the driver can then take the necessary actions to ensure a safe journey. We propose a method for traffic sign detection and recognition using ensemble techniques on four models, namely BEiT, Yolo V5, Faster-CNN, and sequential CNN. Current research focuses on traffic sign detection using an individual model like CNN. To further boost the accuracy of object detection, our proposed approach uses a combination of the average, AND, OR, and weighted-fusion strategies to combine the outputs of the different ensembles. The testing in this project utilizes the German Traffic Sign Recognition Benchmark (GTSRB), Belgium Traffic Sign image data, and Road Sign Detection datasets. In comparison with the individual models for object detection, the ensemble of these models was able to increase the model accuracy to 99.54% with validation accuracy of 99.74% and test accuracy of 99.34%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Traffic Sign Detection and Recognition Using Ensemble Object Detection Models


    Additional title:

    Lect. Notes in Networks, Syst.



    Conference:

    International Conference On Innovative Computing And Communication ; 2023 ; Delhi, India February 17, 2023 - February 18, 2023



    Publication date :

    2023-10-26


    Size :

    22 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Research on Traffic Sign Detection and Recognition System Using Deep Ensemble Learning

    Wang, Lung-Jen / Suwattanapunkul, Taweelap / Thalauy, Jarinya et al. | IEEE | 2024


    Traffic Sign Detection and Recognition

    Pillai, Preeti S. / Kinnal, Bhagyashree / Pattanashetty, Vishal et al. | Springer Verlag | 2022


    Traffic Sign Detection and Recognition

    Habibi Aghdam, Hamed / Jahani Heravi, Elnaz | Springer Verlag | 2017


    Traffic Sign Detection and Recognition System

    Escalera, Sergio / Baró, Xavier / Pujol, Oriol et al. | Springer Verlag | 2011


    Indian Traffic Sign Detection and Recognition

    Alam, Altaf / Jaffery, Zainul Abdin | Springer Verlag | 2020