Fast and Automatic Object Registration for Human-Robot Collaboration in Industrial Manufacturing

We present an end-to-end framework for fast retraining of object detection models in human-robot-collaboration. Our Faster R-CNN based setup covers the whole workflow of automatic image generation and labeling, model retraining on-site as well as inference on a FPGA edge device. The intervention of...

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Bibliographic Details
Published inarXiv.org
Main Authors Geiß, Manuela, Baresch, Martin, Chasparis, Georgios, Schweiger, Edwin, Teringl, Nico, Zwick, Michael
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.04.2022
Subjects
Collaboration
Image processing
Object recognition
Robots
Workflow
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Summary:We present an end-to-end framework for fast retraining of object detection models in human-robot-collaboration. Our Faster R-CNN based setup covers the whole workflow of automatic image generation and labeling, model retraining on-site as well as inference on a FPGA edge device. The intervention of a human operator reduces to providing the new object together with its label and starting the training process. Moreover, we present a new loss, the intraspread-objectosphere loss, to tackle the problem of open world recognition. Though it fails to completely solve the problem, it significantly reduces the number of false positive detections of unknown objects.
ISSN:2331-8422