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dc.contributorEscuela Superior y Tecnica de Ingenieros de Minases_ES
dc.contributor.authorRodríguez-Gonzálvez, Pablo
dc.contributor.authorRodríguez-Martín, Manuel
dc.contributor.otherIngeniería Cartografica, Geodesica y Fotogrametriaes_ES
dc.date2019-01-07
dc.date.accessioned2020-08-13T09:47:12Z
dc.date.available2020-08-13T09:47:12Z
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10612/12349
dc.description14 p.es_ES
dc.description.abstractWeld bead detection is essential for automated welding inspection processes. The non-invasive passive techniques, such as photogrammetry, are quickly evolving to provide a 3D point cloud with submillimeter precision and spatial resolution. However, its application in weld visual inspection has not been extensively studied. The derived 3D point clouds, despite the lack of topological information, store significant information for the weld-plaque segmentation. Although the weld bead detection is being carried out over images or based on laser profiles, its characterization by means of 3D geometrical features has not been assessed. Moreover, it is possible to combine machine learning approaches and the 3D features in order to realize the full potential of the weld bead segmentation of 3D submillimeter point clouds. In this paper, the novelty is focused on the study of 3D features on real cases to identify the most relevant ones for weld bead detection on the basis of the information gain. For this novel contribution, the influence of neighborhood size for covariance matrix computation, decision tree algorithms, and split criteria are analyzed to assess the optimal results. The classification accuracy is evaluated by the degree of agreement of the classified data by the kappa index and the area under the receiver operating characteristic (ROC) curve. The experimental results show that the proposed novel methodology performs better than 0.85 for the kappa index and better than 0.95 for ROC area.es_ES
dc.languageenges_ES
dc.publisherIEEEes_ES
dc.subjectIngenieríases_ES
dc.subject.otherWeldinges_ES
dc.subject.otherMachine learninges_ES
dc.subject.otherDecision treees_ES
dc.subject.otherWeld beades_ES
dc.subject.otherPhotogrammetryes_ES
dc.subject.other3D modeles_ES
dc.subject.otherNon-destructive testinges_ES
dc.titleWeld bead detection based on 3D geometric features and machine learning approacheses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2019.2891367
dc.description.peerreviewedSIes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.journal.titleIEEE Accesses_ES
dc.volume.number7es_ES
dc.page.initial14714es_ES
dc.page.final14727es_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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