{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T17:54:46Z","timestamp":1776621286793,"version":"3.51.2"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T00:00:00Z","timestamp":1615507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFF0205600"],"award-info":[{"award-number":["2017YFF0205600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["3221008101"],"award-info":[{"award-number":["3221008101"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010856","name":"Jiangsu Provincial Department of Transport","doi-asserted-by":"publisher","award":["7621000132"],"award-info":[{"award-number":["7621000132"]}],"id":[{"id":"10.13039\/501100010856","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010856","name":"Jiangsu Provincial Department of Transport","doi-asserted-by":"publisher","award":["7621000133"],"award-info":[{"award-number":["7621000133"]}],"id":[{"id":"10.13039\/501100010856","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu nature science foundation under Grant","award":["BK20181279"],"award-info":[{"award-number":["BK20181279"]}]},{"name":"Science and Technology Project of Zhejiang Provincial Department of Transport under Grant","award":["2020045"],"award-info":[{"award-number":["2020045"]}]},{"name":"Science and Technology Project of Zhejiang Provincial Department of Transport under Grant","award":["2020053"],"award-info":[{"award-number":["2020053"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Improving the detection efficiency and maintenance benefits is one of the greatest challenges in road testing and maintenance. To address this problem, this paper presents a method for combining the you only look once (YOLO) series with 3D ground-penetrating radar (GPR) images to recognize the internal defects in asphalt pavement and compares the effectiveness of traditional detection and GPR detection by evaluating the maintenance benefits. First, traditional detection is conducted to survey and summarize the surface conditions of tested roads, which are missing the internal information. Therefore, GPR detection is implemented to acquire the images of concealed defects. Then, the YOLOv5 model with the most even performance of the six selected models is applied to achieve the rapid identification of road defects. Finally, the benefits evaluation of maintenance programs based on these two detection methods is conducted from economic and environmental perspectives. The results demonstrate that the economic scores are improved and the maintenance cost is reduced by $49,398\/km based on GPR detection; the energy consumption and carbon emissions are reduced by 792,106 MJ\/km (16.94%) and 56,289 kg\/km (16.91%), respectively, all of which indicates the effectiveness of 3D GPR in pavement detection and maintenance.<\/jats:p>","DOI":"10.3390\/rs13061081","type":"journal-article","created":{"date-parts":[[2021,3,14]],"date-time":"2021-03-14T23:52:06Z","timestamp":1615765926000},"page":"1081","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":161,"title":["Application of Combining YOLO Models and 3D GPR Images in Road Detection and Maintenance"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8012-7682","authenticated-orcid":false,"given":"Zhen","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China"}]},{"given":"Wenxiu","family":"Wu","sequence":"additional","affiliation":[{"name":"Highway and Transportation Management Center, Jinhua 321000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8561-7223","authenticated-orcid":false,"given":"Xingyu","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China"}]},{"given":"Shuwei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China"}]},{"given":"Lutai","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China"}]},{"given":"Tianjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang Scientific Research Institute of Transport, Hangzhou 310023, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.patrec.2011.11.004","article-title":"Crack Tree: Automatic crack detection from pavement images","volume":"33","author":"Zou","year":"2012","journal-title":"Pattern Recogn. 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