FasteNet: A Fast Railway Fastener Detector

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    In this work, a novel high-speed railway fastener detector is introduced. This fully convolutional network, dubbed FasteNet, foregoes the notion of bounding boxes and performs detection directly on a predicted saliency map. FasteNet uses transposed convolutions and skip connections, the effective receptive field of the network is 1.5 times larger than the average size of a fastener, enabling the network to make predictions with high confidence, without sacrificing output resolution. In addition, due to the saliency map approach, the network is able to vote for the presence of a fastener up to 30 times per fastener, boosting prediction accuracy. FasteNet is capable of running at 110 FPS on an Nvidia GTX 1080, while taking in inputs of 1600 x 512 with an average of 14 fasteners per image. Our source is open here: FasteNet.git.
    Original languageEnglish
    Title of host publicationProceedings of 6th International Congress on Information and Communication Technology, ICICT 2021
    Subtitle of host publicationICICT 2021, London, Volume 1
    EditorsXin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
    PublisherSpringer Nature
    Number of pages11
    ISBN (Electronic)978-981-16-2102-4
    ISBN (Print)978-981-16-2101-7
    Publication statusPublished - Jan 2022
    Event6th International Congress on Information and Communication Technology - Online, London, United Kingdom
    Duration: 25 Feb 202126 Feb 2021
    Conference number: 6

    Publication series

    NameLecture Notes in Networks and Systems
    ISSN (Print)2367-3370
    ISSN (Electronic)2367-3389


    Conference6th International Congress on Information and Communication Technology
    Abbreviated titleICICT 2021
    Country/TerritoryUnited Kingdom
    Internet address

    Bibliographical note

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    • Convolutional neural network
    • computer vision
    • railway fastener detector
    • Object detection
    • Convolutional neural networks
    • Railway fastener detection

    ASJC Scopus subject areas

    • Signal Processing
    • Control and Systems Engineering
    • Computer Networks and Communications


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