Deep Learning for Real-Time Capable Object Detection and Localization on Mobile Platforms

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(Konferenzbeitrag)


Details zur Publikation

Autorinnen und Autoren: Particke F, Kolbenschlag R, Hiller M, Patino-Studencki L, Thielecke J
Jahr der Veröffentlichung: 2017
Band: 261
Heftnummer: 1
Seitenbereich: 012005
Sprache: Englisch


Abstract


Industry 4.0 is one of the most formative terms in current times. Subject of research are particularly smart and autonomous mobile platforms, which enormously lighten the workload and optimize production processes. In order to interact with humans, the platforms need an in-depth knowledge of the environment. Hence, it is required to detect a variety of static and non-static objects. Goal of this paper is to propose an accurate and real-time capable object detection and localization approach for the use on mobile platforms. A method is introduced to use the powerful detection capabilities of a neural network for the localization of objects. Therefore, detection information of a neural network is combined with depth information from a RGB-D camera, which is mounted on a mobile platform. As detection network, YOLO Version 2 (YOLOv2) is used on a mobile robot. In order to find the detected object in the depth image, the bounding boxes, predicted by YOLOv2, are mapped to the corresponding regions in the depth image. This provides a powerful and extremely fast approach for establishing a real-time-capable Object Locator. In the evaluation part, the localization approach turns out to be very accurate. Nevertheless, it is dependent on the detected object itself and some additional parameters, which are analysed in this paper.



FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Hiller, Markus
Professur für Informationstechnik (Schwerpunkt Ortsbestimmung und Navigation)
Kolbenschlag, Robin
Lehrstuhl für Informationstechnik mit dem Schwerpunkt Kommunikationselektronik (Stiftungslehrstuhl)
Particke, Florian
Professur für Informationstechnik (Schwerpunkt Ortsbestimmung und Navigation)
Patino-Studencki, Lucila
Professur für Informationstechnik (Schwerpunkt Ortsbestimmung und Navigation)
Thielecke, Jörn Prof. Dr.
Professur für Informationstechnik (Schwerpunkt Ortsbestimmung und Navigation)


Zitierweisen

APA:
Particke, F., Kolbenschlag, R., Hiller, M., Patino-Studencki, L., & Thielecke, J. (2017). Deep Learning for Real-Time Capable Object Detection and Localization on Mobile Platforms. In Proceedings of the AIAAT 2017 (pp. 012005). Hawaii, US.

MLA:
Particke, Florian, et al. "Deep Learning for Real-Time Capable Object Detection and Localization on Mobile Platforms." Proceedings of the AIAAT 2017, Hawaii 2017. 012005.

BibTeX: 

Zuletzt aktualisiert 2019-23-07 um 07:35