Embedded vision device integration via OPC UA: Design and evaluation of a neural network-based monitoring system for Industry 4.0

Abstract Sensor application is a basis for digitized industrial value creation. However, for existing production and logistics systems, sensor retrofitting is accompanied by challenges, including plant heterogeneity and lack of standards. This work addresses this issue through the design, implementation and evaluation of an embedded vision high-bay shelf monitoring system of an Industry 4.0 demonstrator. Utilizing design science research methodologies, the artifact unites the concepts of computer vision, convolutional neural networks and OPC UA for widely applicable and cost-efficient retrofitting. Design principles derived from the artifact’s design and evaluation cycles can serve as abstracted guidelines for designing retrofit visual sensor systems.