Instance-level augmentation for synthetic agricultural data using depth maps
dc.contributor.author | Wübben, Henning | |
dc.contributor.author | Butz, Raphaela | |
dc.contributor.author | von Szadkowski, Kai | |
dc.contributor.author | Barenkamp, Marco | |
dc.contributor.editor | Hoffmann, Christa | |
dc.contributor.editor | Stein, Anthony | |
dc.contributor.editor | Ruckelshausen, Arno | |
dc.contributor.editor | Müller, Henning | |
dc.contributor.editor | Steckel, Thilo | |
dc.contributor.editor | Floto, Helga | |
dc.date.accessioned | 2023-02-21T15:13:57Z | |
dc.date.available | 2023-02-21T15:13:57Z | |
dc.date.issued | 2023 | |
dc.description.abstract | Image augmentation is a key component in computer vision pipelines. Its techniques utilize different levels of data annotation. A lack of methods can be observed when it comes to data that supplies depth maps, in particular synthetic data. We propose a novel augmentation method named DepthAug that utilizes depth annotations in image data and examine its performance in the context of object detection tasks. Results show a boost in MAP score performance compared to previous related methods. | en |
dc.identifier.isbn | 978-3-88579-724-1 | |
dc.identifier.pissn | 1617-5468 | |
dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/40258 | |
dc.language.iso | en | |
dc.publisher | Gesellschaft für Informatik e.V. | |
dc.relation.ispartof | 43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme | |
dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-330 | |
dc.subject | image augmentation | |
dc.subject | synthetic data | |
dc.subject | deep image compositing | |
dc.subject | object detection | |
dc.subject | domain gap | |
dc.title | Instance-level augmentation for synthetic agricultural data using depth maps | en |
dc.type | Text/Conference Paper | |
gi.citation.endPage | 278 | |
gi.citation.publisherPlace | Bonn | |
gi.citation.startPage | 267 | |
gi.conference.date | 13.-14. Februar 2023 | |
gi.conference.location | Osnabrück |
Dateien
Originalbündel
1 - 1 von 1
Lade...
- Name:
- GIL_2023_Wuebben_267-278.pdf
- Größe:
- 883.47 KB
- Format:
- Adobe Portable Document Format