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Metadados

1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identificador8JMKD3MGPEW34M/47K7NDL
Repositóriosid.inpe.br/sibgrapi/2022/09.12.07.42
Última Atualização2022:09.12.15.37.18 (UTC) chafic.ac.abou-akar@bmw.de
Repositório de Metadadossid.inpe.br/sibgrapi/2022/09.12.07.42.32
Última Atualização dos Metadados2023:05.23.04.20.42 (UTC) administrator
DOI10.1109/SIBGRAPI55357.2022.9991784
Chave de CitaçãoAbouAkarTeJeKhKaGu:2022:SyObRe
TítuloSynthetic Object Recognition Dataset for Industries
FormatoOn-line
Ano2022
Data de Acesso06 jul. 2025
Número de Arquivos1
Tamanho7717 KiB
2. Contextualização
Autor1 Abou Akar, Chafic
2 Tekli, Jimmy
3 Jess, Daniel
4 Khoury, Mario
5 Kamradt, Marc
6 Guthe, Michael
Afiliação1 BMW Group, Univ. Bourgogne France-Comté
2 BMW Group
3 BMW Group
4 BMW Group
5 BMW Group
6 University of Bayreuth
Endereço de e-Mailchafic.ac.abou-akar@bmw.de
Nome do EventoConference on Graphics, Patterns and Images, 35 (SIBGRAPI)
Localização do EventoNatal, RN
Data24-27 Oct. 2022
Título do LivroProceedings
Tipo TerciárioFull Paper
Histórico (UTC)2022-09-12 15:37:18 :: chafic.ac.abou-akar@bmw.de -> administrator :: 2022
2023-05-23 04:20:42 :: administrator -> :: 2022
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Palavras-ChaveComputer Vision
Industry 4.0
Logistic
Object Recognition
Smart Factory
Synthetic Dataset
ResumoSmart robots in factories highly depend on Computer Vision (CV) tasks, e.g. object detection and recognition, to perceive their surroundings and react accordingly. These CV tasks can be performed after training deep learning (DL) models on large annotated datasets. In an industrial setting, acquiring and annotating such datasets is challenging because it is time-consuming, prone to human error, and limited by several privacy and security regulations. In this study, we propose a synthetic industrial dataset for object detection purposes created using NVIDIA Omniverse. The dataset consists of 8 industrial assets in 32 scenarios and 200,000 photo-realistic rendered images that are annotated with accurate bounding boxes. For evaluation purposes, multiple object detectors were trained with synthetic data to infer on real images captured inside a factory. Accuracy values higher than 50% and up to 100% were reported for most of the considered assets.
Arranjourlib.net > SDLA > Fonds > SIBGRAPI 2022 > Synthetic Object Recognition...
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4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/8JMKD3MGPEW34M/47K7NDL
URL dos dados zipadoshttp://urlib.net/zip/8JMKD3MGPEW34M/47K7NDL
Idiomaen
Arquivo AlvoABOU~AKAR_22_pre-print.pdf
Grupo de Usuárioschafic.ac.abou-akar@bmw.de
Visibilidadeshown
5. Fontes relacionadas
Repositório Espelhosid.inpe.br/banon/2001/03.30.15.38.24
Unidades Imediatamente Superiores8JMKD3MGPEW34M/495MHJ8
Lista de Itens Citandosid.inpe.br/sibgrapi/2023/05.19.12.10 53
sid.inpe.br/sibgrapi/2022/06.10.21.49 13
sid.inpe.br/banon/2001/03.30.15.38.24 7
Acervo Hospedeirosid.inpe.br/banon/2001/03.30.15.38
6. Notas
Campos Vaziosarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition editor electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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