FasteNet: A Fast Railway Fastener Detector

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Abstract

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: https://github.com/jjshoots/DL 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
Pages767-777
Number of pages11
ISBN (Electronic)978-981-16-2102-4
ISBN (Print)978-981-16-2101-7
DOIs
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
https://www.icict.co.uk/

Publication series

NameLecture Notes in Networks and Systems
Volume235
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference6th International Congress on Information and Communication Technology
Abbreviated titleICICT 2021
Country/TerritoryUnited Kingdom
CityLondon
Period25/02/2126/02/21
Internet address

Bibliographical note

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Keywords

  • 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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