{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:18:34Z","timestamp":1776680314946,"version":"3.51.2"},"reference-count":156,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T00:00:00Z","timestamp":1723161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42271450"],"award-info":[{"award-number":["42271450"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cracks are a common defect in civil infrastructures, and their occurrence is often closely related to structural loading conditions, material properties, design and construction, and other factors. Therefore, detecting and analyzing cracks in civil infrastructures can effectively determine the extent of damage, which is crucial for safe operation. In this paper, Web of Science (WOS) and Google Scholar were used as literature search tools and \u201ccrack\u201d, \u201ccivil infrastructure\u201d, and \u201ccomputer vision\u201d were selected as search terms. With the keyword \u201ccomputer vision\u201d, 325 relevant documents were found in the study period from 2020 to 2024. A total of 325 documents were searched again and matched with the keywords, and 120 documents were selected for analysis and research. Based on the main research methods of the 120 documents, we classify them into three crack detection methods: fusion of traditional methods and deep learning, multimodal data fusion, and semantic image understanding. We examine the application characteristics of each method in crack detection and discuss its advantages, challenges, and future development trends.<\/jats:p>","DOI":"10.3390\/rs16162910","type":"journal-article","created":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T09:48:54Z","timestamp":1723196934000},"page":"2910","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["A Review of Computer Vision-Based Crack Detection Methods in Civil Infrastructure: Progress and Challenges"],"prefix":"10.3390","volume":"16","author":[{"given":"Qi","family":"Yuan","sequence":"first","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6487-0713","authenticated-orcid":false,"given":"Yufeng","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Mingyue","family":"Li","sequence":"additional","affiliation":[{"name":"School of Foreign Languages, Nanjing Forestry University, Nanjing 210037, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Azimi, M., Eslamlou, A.D., and Pekcan, G. 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