Traffic sign detection is one of the most important tasks for autonomouspublic transport vehicles. It provides a global view of the traffic signs on the road. Inthis chapter, we introduce a traffic sign detection method based on auto-encodersand Convolutional Neural Networks. For this purpose, we propose an end-to-endunsupervised/supervised learning method to solve a traffic sign detection task. Themain idea of the proposed approach aims to perform an interconnection between anauto-encoder and a Convolutional Neural Networks to act as a single network to detecttraffic signs under real-world conditions. The auto-encoder enhances the resolution ofthe input images and the convolutional neural network was used to detect and identifytraffic signs. Besides, to build a traffic signs detector with high performance, weproposed a new traffic sign dataset. It contains more classes than the existing ones,which contain 10000 images from 73 traffic sign classes captured on the Chinese roads.The proposed detector proved its efficiency when evaluated on the custom dataset byachieving a mean average precision of 86.42%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Traffic Sign Detection for Smart Public Transport Vehicles: Cascading Convolutional Autoencoder With Convolutional Neural Network


    Contributors:


    Publication date :

    2022




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    German Traffic Sign Recognition Using Convolutional Neural Network

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


    Enhancing the Traffic Sign Detection to Improve Convolutional Neural Network

    Sungheetha, Akey / V, Vijeya kaveri / V, Sruthi Sri et al. | IEEE | 2024




    Traffic Data Imputation with Ensemble Convolutional Autoencoder

    Ye, Yongchao / Zhang, Shuyu / Yu, James J.Q. | IEEE | 2021