{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T09:10:33Z","timestamp":1780564233383,"version":"3.54.1"},"reference-count":68,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:00:00Z","timestamp":1626739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation Graduate Research Fellowship","award":["DGE-1656466"],"award-info":[{"award-number":["DGE-1656466"]}]},{"name":"Department of Civil and Environmental Engineering, Princeton University","award":["n\/a"],"award-info":[{"award-number":["n\/a"]}]},{"name":"Dean's Fund for Innovation, Princeton University","award":["n\/a"],"award-info":[{"award-number":["n\/a"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21144929","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T11:26:10Z","timestamp":1626780370000},"page":"4929","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Crack Detection in Images of Masonry Using CNNs"],"prefix":"10.3390","volume":"21","author":[{"given":"Mitchell J.","family":"Hallee","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8939-5998","authenticated-orcid":false,"given":"Rebecca K.","family":"Napolitano","sequence":"additional","affiliation":[{"name":"Department of Architectural Engineering, Pennsylvania State University, University Park, PA 16802, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7256-2123","authenticated-orcid":false,"given":"Wesley F.","family":"Reinhart","sequence":"additional","affiliation":[{"name":"Department of Materials Science and Engineering, Pennsylvania State University, University Park, PA 16802, USA"},{"name":"Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA 16802, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1852-5310","authenticated-orcid":false,"given":"Branko","family":"Glisic","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1680\/coma.12.00033","article-title":"Rethinking structural masonry: Unreinforced, stone-cut shells","volume":"166","author":"Rippmann","year":"2013","journal-title":"Proc. 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