Crack Detection in Images of Masonry Using CNNs.

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

doi:10.3390/s21144929

Mitchell J Hallee, Wesley F Reinhart + Show 2 more

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https://doi.org/10.3390/s21144929

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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.

Highlights

Masonry construction is common in both historical and contemporary architecture [1,2,3].there has been a surge of interest in using masonry for sustainable infrastructure in the future [4,5,6,7,8]
We produced a new dataset consisting of 2542 labeled image patches of masonry walls in a controlled laboratory environment. These data were used to train three different convolutional neural network (CNN) architectures to classify image patches as cracked or uncracked, a challenging problem which has been the subject of several recent studies by other authors
The results show that the same CNN architecture which was sufficient for concrete and asphalt cracking in Ref. [36] is not sufficient for crack detection in masonry structures

Summary

Introduction

Masonry construction is common in both historical and contemporary architecture [1,2,3].there has been a surge of interest in using masonry for sustainable infrastructure in the future [4,5,6,7,8]. Masonry construction is common in both historical and contemporary architecture [1,2,3]. There are a myriad of ways these structures can incur damage over their lifetime. Masonry structures are susceptible to cracking due to thermal stress from freezing and thawing cycles [9,10,11] or incompatible material adjacency [12], hydroscopic stress from precipitation or rising damp [13,14], as well as mechanical stress from settlement [15,16,17,18] or earthquakes [19]. Unreinforced masonry is typically the most vulnerable type of building material to earthquake damage according to the U.S Federal Emergency Management Agency [20]

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