Object Detection for Smart Factory Processes by Machine Learning

Item Type Conference or Workshop Item (unspecified)
Abstract

The production industry is in a transformation towards more autonomous and intelligent manufacturing. In addition to more flexible production processes to dynamically respond to changes in the environment, it is also essential that production processes are continuously monitored and completed in time. Video-based methods such as object detection systems are still in their infancy and rarely used as basis for process monitoring. In this paper, we present a framework for video-based monitoring of manufacturing processes with the help of a physical smart factory simulation model. We evaluate three state-of-the-art object detection systems regarding their suitability to detect workpieces and to recognize failure situations that require adaptations. In our experiments, we are able to show that detection accuracies above 90% can be achieved with current object detection methods.

Authors Malburg, Lukas; Rieder, Manfred-Peter; Seiger, Ronny; Klein, Patrick & Bergmann, Ralph
Language English
Subjects computer science
HSG Classification contribution to scientific community
Date May 2021
Publisher Elsevier
Series Name Procedia Computer Science
Volume 184
Number of Pages 581
Publisher DOI 10.1016/j.procs.2021.04.009
Official URL https://doi.org/10.1016/j.procs.2021.04.009
Additional Information See for a demo video: https://doi.org/10.6084/m9.figshare.13240784
Depositing User Dr.-Ing. Ronny Seiger
Date Deposited 18 May 2021 10:49
Last Modified 03 Sep 2021 13:16
URI: https://www.alexandria.unisg.ch/publications/263172

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Malburg, Lukas; Rieder, Manfred-Peter; Seiger, Ronny; Klein, Patrick & Bergmann, Ralph: Object Detection for Smart Factory Processes by Machine Learning. [Conference or Workshop Item]

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https://www.alexandria.unisg.ch/id/eprint/263172
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