Road crack detection using a single stage detector based deep neural network

Thomas Arthur Carr, Mark David Jenkins, Maria Insa Iglesias, Tom Buggy, Gordon Morison

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Condition and deterioration of public and private infrastructure is an issue that directly affects the majority of the world population. In this paper we propose the application of a Residual Neural Network to automatically detect road and pavement surface cracks. The high amount of variance in the texture of the surface and variation in illumination levels makes the task of automatically detecting defects within public and private infrastructure a difficult task. The system developed utilises a feature pyramid core with an underlying feed-forward ResNet architecture. The output from the feature pyramid then feeds into two sub-networks. One sub-network associates a class with the output from the feature pyramid. The other sub-network regresses the offset from each of the output bounding boxes of the feature pyramid to the corresponding ground truth boxes during training. The network was trained on real world data from an already established dataset. The data used to train and test on is very limited, due to the lack of available road crack datasets in the public domain. Despite the limited amount of data, the proposed method achieves a very positive results with minimal error.
Original languageEnglish
Title of host publication2018 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS)
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)9781538664056
ISBN (Print)9781538664063
DOIs
Publication statusPublished - 9 Jul 2018

Keywords

  • image segmentation
  • neural networks
  • roads
  • training
  • surface cracks
  • machine learning
  • computer architecture

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  • Cite this

    Carr, T. A., Jenkins, M. D., Iglesias, M. I., Buggy, T., & Morison, G. (2018). Road crack detection using a single stage detector based deep neural network. In 2018 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) (pp. 1-5). IEEE. https://doi.org/10.1109/EESMS.2018.8405819