Deep convolutional neural networks for detection of rail surface defects

Shahrzad Faghih Roohi, Siamak Hajizadeh, Alfredo Nunez, Robert Babuska, Bart De Schutter

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

153 Citations (Scopus)
950 Downloads (Pure)

Abstract

In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and detect rail surface defects. Therefore, automated detection of rail defects can help to save time and costs, and to ensure rail transportation safety. However, one major challenge is that the extraction of suitable features for detection of rail surface defects is a non-trivial and difficult task. Therefore, we propose to use convolutional neural networks as a viable technique for feature learning. Deep convolutional neural networks have recently been applied to a number of similar domains with success. We compare the results of different network architectures characterized by different sizes and activation functions. In this way, we explore the efficiency of the proposed deep convolutional neural network for detection and classification. The experimental results are promising and demonstrate the capability of the proposed approach.
Original languageEnglish
Title of host publicationProceedings 2016 International Joint Conference on Neural Networks (IJCNN)
EditorsPablo A. Estevez, Plamen P. Angelov, Emilio Del Moral Hernandez
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages2584-2589
ISBN (Print)978-1-5090-0619-9
DOIs
Publication statusPublished - 2016
EventIJCNN 2016: International Joint Conference on Neural Networks - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Conference

ConferenceIJCNN 2016
CountryCanada
CityVancouver
Period24/07/1629/07/16

Keywords

  • Rails
  • Neural networks
  • Convolution
  • Training
  • Feature extraction
  • Insulation life
  • Degradation

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