Introducing a Decision Support System for Use-Case Specific Object Detection Methods in Production Systems

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dc.identifier.uri http://dx.doi.org/10.15488/11248
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/11335
dc.contributor.author Müller, Kai
dc.contributor.author Saß, Stephan-Andrés
dc.contributor.author Greb, Christoph
dc.contributor.editor Herberger, David
dc.contributor.editor Hübner, Marco
dc.date.accessioned 2021-08-19T08:32:16Z
dc.date.issued 2021
dc.identifier.citation Müller, K.; Saß, S.-A.; Greb, C.: Introducing a Decision Support System for Use-Case Specific Object Detection Methods in Production Systems. In: Herberger, D.; Hübner, M. (Eds.): Proceedings of the Conference on Production Systems and Logistics : CPSL 2021. Hannover : publish-Ing., 2021, S. 605-615. DOI: https://doi.org/10.15488/11248
dc.description.abstract Object detection has the potential to facilitate the automation of optical quality inspection, achieving a significant reduction of human error. However, the gap between needed expertise to understand and integrate complex object detection systems into production environments and the availability of computer scientists, is hindering its use in the manufacturing industry. A support system for decision-makers unrelated to the subject is therefore required to promote industry utilisation of object detection and effectively manage this otherwise unused opportunity of profitable knowledge. Within the Cluster of Excellency “Internet of Production (IoP)”, such a support system has been developed. Lowering the implementation hurdle of object detection systems is achieved by translating complex information about existing methods into tangible factors, such as quality and cost. In this work we aim to structure relevant object detection techniques and employ a decision tree to provide a user-support based on a use-case-oriented framework. The use-case’s basic conditions and requirements serve as input for the framework. The decision tree gives the suitable object detection method as output, accordingly. For traditional object detection techniques, the characteristics are translated into basic requirements, which are then used as input, e.g., for the template matching method the comparison of a source image with a reference image is translated to the ability to guarantee images for all possible quality deviations. The deep learning (DL) methods are consolidated in the project management triangle consisting of quality, cost, and time. Firstly, an introduction into object detection is given. Secondly, traditional methods are clustered and deep learning methods classified. A description of the decision tree is then presented, before testing results conclude this paper. The developed support system enables decision-makers to evaluate object detection methods for individual use-cases and consequently achieve increased production planning efficiency. The system’s universal design allows for application across manufacturing industries and use-cases. eng
dc.language.iso eng
dc.publisher Hannover : publish-Ing.
dc.relation.ispartof https://doi.org/10.15488/11229
dc.relation.ispartof Proceedings of the Conference on Production Systems and Logistics : CPSL 2021
dc.rights CC BY 3.0 DE
dc.rights.uri https://creativecommons.org/licenses/by/3.0/de/
dc.subject Object Detection eng
dc.subject Computer Vision eng
dc.subject Deep Learning eng
dc.subject Quality Management eng
dc.subject Digitalisation eng
dc.subject Automation eng
dc.subject Internet of Production eng
dc.subject.classification Konferenzschrift ger
dc.subject.ddc 620 | Ingenieurwissenschaften und Maschinenbau
dc.title Introducing a Decision Support System for Use-Case Specific Object Detection Methods in Production Systems eng
dc.type bookPart
dc.type Text
dc.relation.essn 2701-6277
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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