DEVELOPMENT OF HIGH-RESOLUTION DETECTOR FOR CRACK AND PATCHING BY USING CONVOLUTIONAL NEURAL NETWORK

In this study, the authors developed the high-resolution detector for crack and patching on pavement of expressway by using convolutional neural network (CNN), and calculated the crack ratio by this method. First, small images such as crack and patching area was randomly sampled using sketch images. The authors set several image sizes to make the judgment by CNN. Then, the size and the judgment accuracy of the model were compared. As a result, over learning of models was observed for larger sizes, and accuracy of model reached a peak at an intermediate size. Cracks and patching were detected by the learned model, and the crack rate was calculated based on the conventional method. From the above results, it was shown that the accuracy of crack ratio is high with the judgment image size of 90 pixels, and the crack shape can be visualized in detail.

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  • English
  • Japanese

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Filing Info

  • Accession Number: 01698962
  • Record Type: Publication
  • Source Agency: Japan Science and Technology Agency (JST)
  • Files: TRIS, JSTAGE
  • Created Date: Jan 25 2019 3:32PM