{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T19:11:46Z","timestamp":1780600306793,"version":"3.54.1"},"reference-count":47,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T00:00:00Z","timestamp":1710720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FE Steel Corporation","award":["DE210101181"],"award-info":[{"award-number":["DE210101181"]}]},{"name":"FE Steel Corporation","award":["DE210101181"],"award-info":[{"award-number":["DE210101181"]}]},{"DOI":"10.13039\/501100000923","name":"Japan Grant for spread of steel bridges in Sri Lanka","doi-asserted-by":"publisher","award":["DE210101181"],"award-info":[{"award-number":["DE210101181"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Japan Grant for spread of steel bridges in Sri Lanka","doi-asserted-by":"publisher","award":["DE210101181"],"award-info":[{"award-number":["DE210101181"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DE210101181"],"award-info":[{"award-number":["DE210101181"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DE210101181"],"award-info":[{"award-number":["DE210101181"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface crack detection is an integral part of infrastructure health surveys. This work presents a transformative shift towards rapid and reliable data collection capabilities, dramatically reducing the time spent on inspecting infrastructures. Two unmanned aerial vehicles (UAVs) were deployed, enabling the capturing of images simultaneously for efficient coverage of the structure. The suggested drone hardware is especially suitable for the inspection of infrastructure with confined spaces that UAVs with a broader footprint are incapable of accessing due to a lack of safe access or positioning data. The collected image data were analyzed using a binary classification convolutional neural network (CNN), effectively filtering out images containing cracks. A comparison of state-of-the-art CNN architectures against a novel CNN layout \u201cCrackClassCNN\u201d was investigated to obtain the optimal layout for classification. A Segment Anything Model (SAM) was employed to segment defect areas, and its performance was benchmarked against manually annotated images. The suggested \u201cCrackClassCNN\u201d achieved an accuracy rate of 95.02%, and the SAM segmentation process yielded a mean Intersection over Union (IoU) score of 0.778 and an F1 score of 0.735. It was concluded that the selected UAV platform, the communication network, and the suggested processing techniques were highly effective in surface crack detection.<\/jats:p>","DOI":"10.3390\/s24061936","type":"journal-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T06:58:39Z","timestamp":1710745119000},"page":"1936","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Deep Learning Approach for Surface Crack Classification and Segmentation in Unmanned Aerial Vehicle Assisted Infrastructure Inspections"],"prefix":"10.3390","volume":"24","author":[{"given":"Shamendra","family":"Egodawela","sequence":"first","affiliation":[{"name":"School of Engineering, RMIT University, 124 La Trobe St, Melbourne, VIC 3000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4800-6554","authenticated-orcid":false,"given":"Amirali","family":"Khodadadian Gostar","sequence":"additional","affiliation":[{"name":"School of Engineering, RMIT University, 124 La Trobe St, Melbourne, VIC 3000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. A. D. Samith","family":"Buddika","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Peradeniya, Peradeniya 20400, Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. J.","family":"Dammika","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Peradeniya, Peradeniya 20400, Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9232-4672","authenticated-orcid":false,"given":"Nalin","family":"Harischandra","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Peradeniya, Peradeniya 20400, Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7772-4531","authenticated-orcid":false,"given":"Satheeskumar","family":"Navaratnam","sequence":"additional","affiliation":[{"name":"School of Engineering, RMIT University, 124 La Trobe St, Melbourne, VIC 3000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mojtaba","family":"Mahmoodian","sequence":"additional","affiliation":[{"name":"School of Engineering, RMIT University, 124 La Trobe St, Melbourne, VIC 3000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1177\/1475921703036169","article-title":"Review Paper: Health Monitoring of Civil Infrastructure","volume":"2","author":"Chang","year":"2003","journal-title":"SAGE Struct. 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