Deep Learning with Spatial Constraint for Tunnel Crack Detection

ABSTRACTCracks are the most common defect on the surface of tunnels, which potentially brings threaten to the safety of the tunnel and the running vehicles. Timely repairing of the crack is of critical importance. In the past two decades, various vehicle platforms have been developed on the purpose of efficient crack detection and maintenance. With these platforms, images can be captured in a traffic speed, and automatic methods can be developed for fast crack localization. However, for image-based crack detection, traditional methods often meet difficulties in handling cracks with low contrast and poor continuity. In this paper, deep learning based techniques are exploited for feature learning and representation for crack detection. A novel deep neural network is presented for pixel-level crack recognition. Hierarchical features in different stages of the convolution are fused together to overcome the influence of noise and a spatial constraint placed on the target pixels is used to guarantee the crack continuity. In the experiment, a tunnel crack dataset is constructed for performance evaluation. Experimental results demonstrate the effectiveness of proposed method.

• Record URL:
  https://doi.org/10.1061/9780784482438.050
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784482438
• Supplemental Notes:
  • © 2019 American Society of Civil Engineers 2019
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Li, Qingquan
  • Zou, Qin
  • Liao, Jianghai
  • Yue, Yuanhao
  • Wang, Song
• Conference:
  • ASCE International Conference on Computing in Civil Engineering 2019
  • Location: Atlanta Georgia, United States
  • Date: 2019-6-17 to 2019-6-19
• Publication Date: 2019

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 393-400
• Monograph Title: Computing in Civil Engineering 2019: Data, Sensing, and Analytics

Subject/Index Terms

• TRT Terms: Cracking; Detection and identification; Machine learning; Neural networks; Vehicular tunnels
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01708147
• Record Type: Publication
• ISBN: 9780784482438
• Files: TRIS, ASCE
• Created Date: Jun 21 2019 10:05AM