Research on Concrete Cracks Recognition based on Dual Convolutional Neural Network

Cracks are the most common and important diseases of concrete bridges. A dual convolutional neural network (DCN) model which is composed of one convolutional neural network (CNN) model and one fully convolutional network (FCN) model is proposed to recognize the cracks in image. Firstly, the CNN model is used to identify the crack area. The interfering factors such as spot, shadow, water stain, and graffiti in the non-crack area will be excluded by CNN model. Then, the CNN results will be segmented by the FCN model with the structure of merging layer-by-layer to extract crack features such as length and width. The DCN model is trained to recognize the actual concrete bridge cracks in this paper. The recognition results show that the DCN model has a good balance between high accuracy and low noise in the process of crack recognition compared with the current image recognition method. The reliability and accuracy of recognition are both greatly improved. The DCN model is helpful for automatic identification of cracks in concrete bridges.

• Record URL:
  • https://doi.org/10.1007/s12205-019-2030-x
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/12267988
• Supplemental Notes:
  • © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2019.
• Authors:
  • Liang, Dong
  • Zhou, Xue-Feng
  • Wang, Song
  • Liu, Chen-Jing
• Publication Date: 2019-7

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: pp 3066-3074
• Serial:
  • KSCE Journal of Civil Engineering
  • Volume: 23
  • Issue Number: 7
  • Publisher: Korean Society of Civil Engineers
  • ISSN: 1226-7988
  • EISSN: 1976-3808
  • Serial URL: http://link.springer.com/journal/12205

Subject/Index Terms

• TRT Terms: Computer models; Concrete bridges; Cracking; Detection and identification technologies; Machine learning; Neural networks
• Subject Areas: Bridges and other structures; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01717270
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 18 2019 5:16PM