DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chung Junyeon | ko |
dc.contributor.author | Sohn, Hoon | ko |
dc.date.accessioned | 2021-05-25T01:30:05Z | - |
dc.date.available | 2021-05-25T01:30:05Z | - |
dc.date.created | 2020-12-23 | - |
dc.date.created | 2020-12-23 | - |
dc.date.issued | 2021-05 | - |
dc.identifier.citation | SMART STRUCTURES AND SYSTEMS, v.27, no.5, pp.783 - 793 | - |
dc.identifier.issn | 1738-1584 | - |
dc.identifier.uri | http://hdl.handle.net/10203/285301 | - |
dc.description.abstract | Bolt loosening is one of the most common types of damage for bolt-connected plates. Existing vision techniques detect bolt loosening based on the measurement of bolt rotation or the exposure of bolt threads. However, these techniques examine bolt tightness only in a qualitative manner, or require a reference measurement at the initially tightened state of the bolt for quantitative estimation. In this study, the exposed shank length of a bolt is quantitatively measured using an RGB-depth camera and a mask-region-based convolutional neural network but without requiring any measurement from the initial state of the bolt. The performance of the proposed technique is validated by conducting lab-scale experiments, in which the angle and distance of the camera are varied with respect to a target inspection area. The proposed technique successfully detects bolt loosening at exposed shank length over 3 mm with a resolution of 1 mm and 97% accuracy at different camera angles (40°–90°) and distances (up to 65 cm). | - |
dc.language | English | - |
dc.publisher | TECHNO-PRESS | - |
dc.title | Detection and quantification of bolt loosening using RGB-D camera and Mask R-CNN | - |
dc.type | Article | - |
dc.identifier.wosid | 000645134400005 | - |
dc.identifier.scopusid | 2-s2.0-85107477216 | - |
dc.type.rims | ART | - |
dc.citation.volume | 27 | - |
dc.citation.issue | 5 | - |
dc.citation.beginningpage | 783 | - |
dc.citation.endingpage | 793 | - |
dc.citation.publicationname | SMART STRUCTURES AND SYSTEMS | - |
dc.identifier.doi | 10.12989/sss.2021.27.5.783 | - |
dc.contributor.localauthor | Sohn, Hoon | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | bolt-loosening detection | - |
dc.subject.keywordAuthor | bolt-loosening quantification | - |
dc.subject.keywordAuthor | RGB-depth camera | - |
dc.subject.keywordAuthor | Mask R-CNN | - |
dc.subject.keywordAuthor | deep learning | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.