Optical 3D Object Recognition for Automated Driving
- In this contribution, we propose an system setup for the detection andclassification of objects in autonomous driving applications. The recognition algo-rithm is based upon deep neural networks, operating in the 2D image domain. Theresults are combined with data of a stereo camera system to finally incorporatethe 3D object information into our mapping framework. The detection systemisIn this contribution, we propose an system setup for the detection andclassification of objects in autonomous driving applications. The recognition algo-rithm is based upon deep neural networks, operating in the 2D image domain. Theresults are combined with data of a stereo camera system to finally incorporatethe 3D object information into our mapping framework. The detection systemis locally running upon the onboard CPU of the vehicle. Several network archi-tectures are implemented and evaluated with respect to accuracy and run-timedemands for the given camera and hardware setup.…
| Author: | Raphael Schwarz, Marin B. Marinov, Stefan HenselGND |
|---|---|
| Editor: | Andreas Christ, Franz Quint |
| Creating Corporation: | Hochschule Offenburg |
| Year of Publication: | 2020 |
| Pagenumber: | 9 |
| ISBN: | 978-3-943301-29-8 (eBook) |
| ISBN: | 978-3-943301-28-1 (Print) |
| Language: | English |
| Parent Title (English): | Artificial Intelligence : Research Impact on Key Industries. Proceedings of the Upper-Rhine Artificial Intelligence Symposium |
| First Page: | 127 |
| Last Page: | 135 |
| Document Type: | Conference Proceeding |
| Open Access: | Frei zugänglich |
| Institutes: | Bibliografie |
| Release Date: | 2020/12/17 |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |
| Note: | Konferenz: The Upper-Rhine Artificial Intelligence Symposium (UR-AI 2020). Collection of accepted papers of the canceled symposium Karlsruhe, 13th May 2020 |
| ArXiv Id: | http://arxiv.org/abs/2010.16241 |