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Synthetic Object Recognition Dataset for Industries

Title data

Abou Akar, Chafic ; Tekli, Jimmy ; Jess, Daniel ; Khoury, Mario ; Kamradt, Marc ; Guthe, Michael:
Synthetic Object Recognition Dataset for Industries.
In: 2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) : Proceedings. - Piscataway, NJ : IEEE , 2022 . - pp. 150-155
ISBN 978-1-6654-5385-1
DOI: https://doi.org/10.1109/SIBGRAPI55357.2022.9991784

Abstract in another language

Smart 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 S 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.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: Training; Shape; Service robots; Computational modeling; Object detection; Cameras; Production facilities
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Professor Applied Computer Science V > Professor Applied Computer Science V - Univ.-Prof. Dr. Michael Guthe
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 07 May 2024 07:36
Last Modified: 07 May 2024 08:31
URI: https://eref.uni-bayreuth.de/id/eprint/89497