| 1. Identity statement | |||||||
| Reference Type | Conference Paper (Conference Proceedings) | ||||||
| Site | sibgrapi.sid.inpe.br | ||||||
| Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH | ||||||
| Identifier | 8JMKD3MGPEW34M/43BD4BE | ||||||
| Repository | sid.inpe.br/sibgrapi/2020/09.30.00.38 | ||||||
| Last Update | 2020:09.30.02.06.02 (UTC) administrator | ||||||
| Metadata Repository | sid.inpe.br/sibgrapi/2020/09.30.00.38.53 | ||||||
| Metadata Last Update | 2022:06.14.00.00.13 (UTC) administrator | ||||||
| DOI | 10.1109/SIBGRAPI51738.2020.00038 | ||||||
| Citation Key | CerpaSalasMezaLoaiBarb:2020:TrSyIm | ||||||
| Title | Training with synthetic images for object detection and segmentation in real machinery images  | ||||||
| Format | On-line | ||||||
| Year | 2020 | ||||||
| Access Date | 2024, Oct. 04 | ||||||
| Number of Files | 1 | ||||||
| Size | 4696 KiB | ||||||
| 2. Context | |||||||
| Author | 1 Cerpa Salas, Alonso Jesús 2 Meza Lovón, Graciela Lecireth 3 Loaiza Fernández, Manuel Eduardo 4 Barbosa Raposo, Alberto | ||||||
| Affiliation | 1 Universidad Católica San Pablo 2 Universidad Católica San Pablo 3 Universidad Católica San Pablo 4 Pontifical Catholic University of Rio de Janeiro | ||||||
| Editor | Musse, Soraia Raupp Cesar Junior, Roberto Marcondes Pelechano, Nuria Wang, Zhangyang (Atlas) | ||||||
| e-Mail Address | alonso.cerpa@ucsp.edu.pe | ||||||
| Conference Name | Conference on Graphics, Patterns and Images, 33 (SIBGRAPI) | ||||||
| Conference Location | Porto de Galinhas (virtual) | ||||||
| Date | 7-10 Nov. 2020 | ||||||
| Publisher | IEEE Computer Society | ||||||
| Publisher City | Los Alamitos | ||||||
| Book Title | Proceedings | ||||||
| Tertiary Type | Full Paper | ||||||
| History (UTC) | 2020-09-30 02:06:04 :: alonso.cerpa@ucsp.edu.pe -> administrator :: 2020 2022-06-14 00:00:13 :: administrator -> alonso.cerpa@ucsp.edu.pe :: 2020 | ||||||
| 3. Content and structure | |||||||
| Is the master or a copy? | is the master | ||||||
| Content Stage | completed | ||||||
| Transferable | 1 | ||||||
| Version Type | finaldraft | ||||||
| Keywords | synthetic data generation object detection object segmentation deep learning | ||||||
| Abstract | Over the last years, Convolutional Neural Networks have been extensively used for solving problems such as image classification, object segmentation, and object detection. However, deep neural networks require a great deal of data correctly labeled in order to perform properly. Generally, generation and labeling processes are carried out by recruiting people to label the data manually. To overcome this problem, many researchers have studied the use of data generated automatically by a renderer. To the best of our knowledge, most of this research was conducted for general-purpose domains but not for specific ones. This paper presents a methodology to generate synthetic data and train a deep learning model for the segmentation of pieces of machinery. For doing so, we built a computer graphics synthetic 3D scenery with the 3D models of real pieces of machinery for rendering and capturing virtual photos from this 3D scenery. Subsequently, we train a Mask R-CNN using the pre-trained weights of COCO dataset. Finally, we obtained our best averages of 85.7% mAP for object detection and 84.8% mAP for object segmentation, over our real test dataset and training only with synthetic images filtered with Gaussian Blur. | ||||||
| Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2020 > Training with synthetic... | ||||||
| Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > Training with synthetic... | ||||||
| doc Directory Content | access | ||||||
| source Directory Content |
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| agreement Directory Content |
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| 4. Conditions of access and use | |||||||
| data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/43BD4BE | ||||||
| zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/43BD4BE | ||||||
| Language | en | ||||||
| Target File | PID6630889.pdf | ||||||
| User Group | alonso.cerpa@ucsp.edu.pe | ||||||
| Visibility | shown | ||||||
| Update Permission | not transferred | ||||||
| 5. Allied materials | |||||||
| Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 | ||||||
| Next Higher Units | 8JMKD3MGPEW34M/43G4L9S 8JMKD3MGPEW34M/4742MCS | ||||||
| Citing Item List | sid.inpe.br/sibgrapi/2020/10.28.20.46 33 sid.inpe.br/sibgrapi/2022/06.10.21.49 2 | ||||||
| Host Collection | sid.inpe.br/banon/2001/03.30.15.38 | ||||||
| 6. Notes | |||||||
| Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume | ||||||
| 7. Description control | |||||||
| e-Mail (login) | alonso.cerpa@ucsp.edu.pe | ||||||
| update | |||||||