Visual AI applications on smart delivery units

Start Page
19
End Page
24
Citation
IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT 2021)
Contribution to Conference
IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2021  
Publisher DOI
10.1109/GCAIoT53516.2021.9693060
Scopus ID
2-s2.0-85126752741
Publisher
IEEE
Peer Reviewed
true
As actors in an IoT production environment, smart delivery units are tasked with identifying loaded components and acquiring shopfloor events such as consumption of material. Conventional identification procedures rely heavily on tags and markers that are applied on components. For processes that require marker-less identification procedures, AI-based object identification can be incorporated. In this paper, we present a novel integration of such visual applications on smart delivery units. We address the main challenges of this approach, namely the need for computational resources and integration with low-cost components. Additionally, we propose a scalable IoT concept for the distribution of the AI functionalities on those delivery units by utilizing containerized applications. We demonstrate the validity of this AI integration with a real-world implementation on delivery units, tested in an application near environment.
Subjects
Smart Delivery Units
AI-based Object Detection
IoT Production Environment
DDC Class
004: Informatik
600: Technik
620: Ingenieurwissenschaften
Funding(s)
Entwicklung und prototypische Erprobung von intelligenten und modularen Ladungsträgern zur schnelleren und effizienteren Materialversorgung bei der Flugzeugproduktion  
Funding Organisations
Bundesministerium für Wirtschaft und Energie - BMWi  
More Funding Information
Research was funded by the Federal Ministry for Economic Affairs and Energy under grant number 20X1731F.