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- Title
Crack Detection in Images of Masonry Using CNNs.
- Authors
Hallee, Mitchell J.; Napolitano, Rebecca K.; Reinhart, Wesley F.; Glisic, Branko
- Abstract
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.
- Subjects
MASONRY; CONVOLUTIONAL neural networks; ASPHALT concrete; BRICK walls; MACHINE learning; STRUCTURAL health monitoring
- Publication
Sensors (14248220), 2021, Vol 21, Issue 14, p4929
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s21144929