Crack detection in images of masonry using cnns

Mitchell J. Hallee, Rebecca K. Napolitano, Wesley F. Reinhart, Branko Glisic

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


While there is a significant body of research on crack detection by computer vision methods in concrete and asphalt, less attention has been given to masonry. We train a convolutional neural network (CNN) on images of brick walls built in a laboratory environment and test its ability to detect cracks in images of brick-and-mortar structures both in the laboratory and on real-world images taken from the internet. We also compare the performance of the CNN to a variety of simpler classifiers operating on handcrafted features. We find that the CNN performed better on the domain adaptation from laboratory to real-world images than these simple models. However, we also find that performance is significantly better in performing the reverse domain adaptation task, where the simple classifiers are trained on real-world images and tested on the laboratory images. This work demonstrates the ability to detect cracks in images of masonry using a variety of machine learning methods and provides guidance for improving the reliability of such models when performing domain adaptation for crack detection in masonry.

Original languageEnglish (US)
Article number4929
Issue number14
StatePublished - Jul 2 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Information Systems
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Biochemistry


  • Computer vision
  • Convolutional neural network
  • Crack detection
  • Machine learning
  • Masonry
  • Structural health monitoring


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