This paper presents a novel convolutional neural network (CNN)-based traffic sign recognition system and investigates pre- and post-processing methods for enhancing performance. We focus on speed limit signs, the most difficult superclass in the US traffic sign set. The Cuda-convnet is chosen as a suitable model for the traffic sign recognition task with low-resolution training images and limited dataset size. We test on the world's largest public dataset of US traffic signs, the LISA-TS extension, and testing dataset. Compared with the current state-of-the-art aggregated channel features detector that has achieved near-perfect detection accuracy except for US speed limit signs, our approach improves the area under precision–recall curve (AUC) of the speed limit sign detection by more than 5%. We also discuss potential improvements of the CNN-based traffic sign recognition method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Pushing the “Speed Limit”: High-Accuracy US Traffic Sign Recognition With Convolutional Neural Networks


    Contributors:

    Published in:

    Publication date :

    2016-06-01


    Size :

    1040417 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Convolutional Neural Networks for Traffic Sign Recognition

    Wei, Zhonghua / Gu, Heng / Zhang, Ran et al. | TIBKAT | 2021


    Traffic sign recognition using convolutional neural networks

    Boujemaa, Kaoutar Sefrioui / Bouhoute, Afaf / Boubouh, Karim et al. | IEEE | 2017


    Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural Networks

    Jin, Junqi / Fu, Kun / Zhang, Changshui | IEEE | 2014


    Real-Time Traffic Sign Recognition Using Convolutional Neural Networks

    Rao, Aditya / Motwani, Rahul / Sarguroh, Naveed et al. | Springer Verlag | 2021


    German Traffic Sign Recognition Using Convolutional Neural Network

    Santosh, G V S Sree / Kumar, G Chaitanya / Sandeep, G et al. | IEEE | 2022