FasteNet: A Fast Railway Fastener Detector

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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 Sixth International Congress on Information and Communication Technology
PublisherSpringer Nature
Pages(In-press)
Number of pages8
Volume(In-press)
Publication statusAccepted/In press - 11 Dec 2020
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

Name Lecture Notes in Networks and Systems
PublisherSpringer Nature

Conference

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

Keywords

  • Convolutional neural network
  • computer vision
  • railway fastener detector

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