Traffic sign inventory is an important part of road safety and traffic management. Road maintenance services perform this task with periodical on-site inspections, monitoring the physical presence and integrity of each object. Due to the increasing complexity of road infrastructure and the high number of objects, this task is usually time-consuming. Object detection based on artificial neural networks and state-of-theart 360° camera equipment may be used to enhance the work of road maintenance services. By automatically capturing images during mandatory inspection drives, detecting traffic signs in those images and linking them with corresponding database entries, semi-automatic inventory and off-site inspections will be possible. This paper describes a system for semi-automatic traffic sign inventory that, in contrast to other approaches, automatically detects and classifies various traffic sign types in 360° images. It then references those signs in an existing inventory database, using GPS and distance estimation and allows for a virtual reality like off-site inspections with a 360° view.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Neural network-based traffic sign recognition in 360° images for semi-automatic road maintenance inventory


    Contributors:


    Publication date :

    2020-09-20


    Size :

    435310 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Road traffic sign recognition based on lightweight neural network

    Li, Le-yang / Yue, Qin / Luo, Rui-wen | SPIE | 2021


    Traffic sign recognition system for inventory purposes

    Maldonado-Bascon, S. / Lafuente-Arroyo, S. / Siegmann, P. et al. | IEEE | 2008


    Traffic Sign Recognition System for Inventory Purposes

    Maldonado-Bascon, S. / Lafuente-Arroyo, S. / Siegmann, P. et al. | British Library Conference Proceedings | 2008


    TRAFFIC SIGN INVENTORY FROM GOOGLE STREET VIEW IMAGES

    V. J. D. Tsai / J.-H. Chen / H.-S. Huang | DOAJ | 2016

    Free access

    German Traffic Sign Recognition Using Convolutional Neural Network

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