Corpus ID: 222295590

KrakN: Transfer Learning framework for thin crack detection in infrastructure maintenance.

@article{arski2020KrakNTL,
  title={KrakN: Transfer Learning framework for thin crack detection in infrastructure maintenance.},
  author={Mateusz Żarski and Bartosz W{\'o}jcik and Jaroslaw Adam Miszczak},
  journal={arXiv: Computer Vision and Pattern Recognition},
  year={2020}
}
  • Mateusz Żarski, Bartosz Wójcik, Jaroslaw Adam Miszczak
  • Published 2020
  • Computer Science
  • arXiv: Computer Vision and Pattern Recognition
  • Monitoring the technical condition of infrastructure is a crucial element to its maintenance. Currently applied methods are outdated, labour-intensive and inaccurate. At the same time, the latest methods using Artificial Intelligence techniques are severely limited in their application due to two main factors -- labour-intensive gathering of new datasets and high demand for computing power. We propose to utilize custom made framework -- KrakN, to overcome these limiting factors. It enables the… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 40 REFERENCES
    Achievements and Challenges in Machine Vision-Based Inspection of Large Concrete Structures
    • 52
    Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces with a Recurrent Neural Network
    • 29
    Automated recognition of surface defects using digital color image processing
    • 81
    Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network
    • 89
    Automatic concrete cracks detection and mapping of terrestrial laser scan data
    • 20
    • PDF
    Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network
    • 46
    Autonomous concrete crack detection using deep fully convolutional neural network
    • 102