{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:59:24Z","timestamp":1791939564841,"version":"4.3.4"},"reference-count":23,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T00:00:00Z","timestamp":1621468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As the frequency of earthquakes has increased in Korea in recent years, designing earthquake-resistant facilities has been increasingly emphasized. Structures constructed with rebars are vulnerable to shaking, which reduces their seismic performance and may result in damage to human life and property. Because the construction of facilities requires the maintenance of sub-constructions, such as by cutting rebars or compensating for missing rebars, information on rebar diameter is required. In this study, the YOLO-v3 algorithm, which has the fastest object recognition performance, was applied to the structural correction data, and a basic experiment was conducted in the air to predict the diameter of rebars in a facility, in real time based on ground-penetrating radar data. The reason for using the YOLO-v3 algorithm is that in the case of GPR data that change slightly according to the diameter of the reinforcing bar, it is difficult to discriminate with the naked eye, and the result may change depending on the inspector. The model achieved a higher accuracy than conventional rebar detection and diameter prediction methods. In addition, the possibility of real-time rebar diameter prediction during construction, using the proposed method, was verified.<\/jats:p>","DOI":"10.3390\/rs13102011","type":"journal-article","created":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T11:45:57Z","timestamp":1621511157000},"page":"2011","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Improvement of GPR-Based Rebar Diameter Estimation Using YOLO-v3"],"prefix":"10.3390","volume":"13","author":[{"given":"Sehwan","family":"Park","sequence":"first","affiliation":[{"name":"Safety Inspection for Infrastructure Laboratory, Advanced Institute of Convergence Technology, Suwon 16229, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinpyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Computer Vision and Artificial Intelligence Laboratory, Advanced Institute of Convergence Technology, Suwon 16229, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyoyoung","family":"Jeon","sequence":"additional","affiliation":[{"name":"R&amp;D Institute, LOTTE Engineering &amp; Construction, Seoul 06515, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6503-7950","authenticated-orcid":false,"given":"Junkyeong","family":"Kim","sequence":"additional","affiliation":[{"name":"Safety Inspection for Infrastructure Laboratory, Advanced Institute of Convergence Technology, Suwon 16229, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6265-7652","authenticated-orcid":false,"given":"Seunghee","family":"Park","sequence":"additional","affiliation":[{"name":"School of Civil, Architectural Engineering &amp; Landscape Architecture, Sungkyunkwan University, Suwon 16419, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"383","DOI":"10.7781\/kjoss.2016.28.6.383","article-title":"Seismic Behavior of 3-Story Steel Frame Structures Subjected to Ground Motions","volume":"28","author":"Hu","year":"2016","journal-title":"J. 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