Traffic signs play a crucial role in regulating traffic and facilitating cautious driving. Automatic traffic sign recognition is one of the key tasks in autonomous driving. Accuracy in the classification of traffic signs is therefore very important for the navigation of a vehicle. Here, a reliable and robust convolutional neural network (CNN) is presented for classifying these signs. The proposed classifier is a weighted multi‐CNN trained with a novel methodology. It achieves a near state‐of‐the‐art recognition rate of 99.59% when tested on the German traffic sign recognition benchmark dataset. Compared to the existing classifiers, the proposed one is a low‐complexity network that recognises a test image in 10 ms when running on an NVIDIA 980 Ti GPU system. The results demonstrate its suitability and reliability in high‐speed driving scenarios.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Traffic sign recognition using weighted multi-convolutional neural network

    Natarajan, Sudha / Annamraju, Abhishek Kumar / Baradkar, Chaitree Sham | IET | 2018

    Freier Zugriff

    Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network

    Luo, Hengliang / Yang, Yi / Tong, Bei et al. | IEEE | 2018


    German Traffic Sign Recognition Using Convolutional Neural Network

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


    Convolutional Neural Networks for Traffic Sign Recognition

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


    Traffic Sign Board Recognition and Voice Alert System using Convolutional Neural Network

    Sukhani, Krish / Shankarmani, Radha / Shah, Jay et al. | IEEE | 2021