Recent years have seen major advances in Artificial Intelligence (AI) methods for environment perception in intelligent transportation systems. Although most of them have been achieved in the automotive sector there is a similar demand in the railway domain. This paper investigates Deep Neural Network (DNN) based environment perception using vehicle-borne camera images from the rail domain. Specifically, railway switch detection and classification are addressed as a relevant example for a DNN application with potential use for landmark positioning, environment perception, and condition monitoring. The lack of large training data sets in the railway sector (in contrast to the automotive domain) is compensated by an appropriate DNN architecture, an anchor box ratio optimization scheme, and transfer learning. The presented experimental results advocate for the adopted approach.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Neural Networks for Railway Switch Detection and Classification Using Onboard Camera Images


    Contributors:


    Publication date :

    2021-12-05


    Size :

    515622 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Railway Pedestrian Intrusion Detection Using Onboard Forward-Viewing Camera

    Li, Yongling / Fu, Bing / Qin, Yong et al. | Springer Verlag | 2022


    Railway Pedestrian Intrusion Detection Using Onboard Forward-Viewing Camera

    Li, Yongling / Fu, Bing / Qin, Yong et al. | British Library Conference Proceedings | 2022


    Railway Pedestrian Intrusion Detection Using Onboard Forward-Viewing Camera

    Li, Yongling / Fu, Bing / Qin, Yong et al. | TIBKAT | 2022


    Traffic Congestion Detection from Camera Images using Deep Convolution Neural Networks

    Chakraborty, Pranamesh / Adu-Gyamfi, Yaw Okyere / Poddar, Subhadipto et al. | Transportation Research Record | 2018


    Toward Railway Automated Defect Detection From Onboard Data Using Deep Learning

    Afzalan, Milad / Jazizadeh, Farrokh / Ahmadian, Mehdi | British Library Conference Proceedings | 2020