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Instance-level augmentation for synthetic agricultural data using depth maps

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2023

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Gesellschaft für Informatik e.V.

Zusammenfassung

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.

Beschreibung

Wübben, Henning; Butz, Raphaela; von Szadkowski, Kai; Barenkamp, Marco (2023): Instance-level augmentation for synthetic agricultural data using depth maps. 43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-724-1. pp. 267-278. Osnabrück. 13.-14. Februar 2023

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