Tunnel Lining Crack Detection Method by Means of Deep Learning

Existing image processing programs for detecting structural damage such as cracks have required the fine-tuning of numerous parameters and experience-based expertise. A method for distinguishing different types of cracks applying deep learning has been developed using tunnel lining images. A classifier was created after learning from a large volume of images in two groups - either with "presence of a crack" or "absence of a crack." The classifier successfully recognized the presence or absence of cracks in images at a rate of more than 90%. Using a color-coded pixelated image to show the position of probable cracks, this paper proposes a hybrid detection method for analyzing cracks with a focus on their location and direction of progress.

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  • English
  • Japanese

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  • Accession Number: 01699693
  • Record Type: Publication
  • Source Agency: Japan Science and Technology Agency (JST)
  • Files: TRIS, JSTAGE
  • Created Date: Feb 23 2019 3:09PM