Patch-based crack detection in black box road images using deep learning

Somin Park, Seongdeok Bang, Hongjo Kim, Hyoungkwan Kim

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)

Abstract

This paper proposes a method for patch-based crack detection of black box road images, for efficient road pavement monitoring. The proposed method is based on deep learning and consists of two modules: road extraction and crack detection. The road extraction module uses the segmentation process of a Fully Convolutional Network (FCN) called FCN-8s to leave only the road area in the image. The crack detection module performs patch-based crack detection on the extracted road area using a convolutional neural network. To the best of the authors’ knowledge, the proposed method is the first attempt to detect road cracks of black box images, which are not orthogonal but skewed actual road images.

Other

Other35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018
Country/TerritoryGermany
CityBerlin
Period18/7/2018/7/25

Bibliographical note

Funding Information:
This work was supported by a grant (18CTAP-C133290-02) from Infrastructure and transportation technology promotion research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Publisher Copyright:
© ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Building and Construction
  • Artificial Intelligence

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