Crack Detection in Images of Masonry Using CNNs

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 14; p. 4929
• Main Authors: Hallee, Mitchell J., Napolitano, Rebecca K., Reinhart, Wesley F., Glisic, Branko
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 20-07-2021; MDPI
• Subjects: Adaptation; Asphalt; Automation; Computer vision; Concrete; convolutional neural network; crack detection; Cracks; Domains; Earthquakes; Flaw detection; Inspections; Laboratories; machine learning; Masonry; Particle size; structural health monitoring; Wavelet transforms
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21144929

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.