{rfName}
Su

Indexed in

License and use

Citations

11

Altmetrics

Analysis of institutional authors

Izquierdo-Domenech, JCorresponding AuthorLinares-Pellicer, JAuthorOrta-Lopez, JAuthor

Share

Publications
>
Proceedings Paper

Supporting interaction in augmented reality assisted industrial processes using a CNN-based semantic layer

Publicated to:2020 Ieee International Conference On Artificial Intelligence And Virtual Reality (Aivr 2020). 27-32 - 2020-01-01 (), DOI: 10.1109/AIVR50618.2020.00014

Authors: Izquierdo-Domenech, Juan; Linares-Pellicer, Jordi; Orta-Lopez, Jorge

Affiliations

Univ Politecn Valencia - Author

Abstract

Even though Augmented Reality (AR) is far from its maturity, we already have solutions and devices that give us an efficient technological frame in different industrial environments. Widely used mobile devices, such as tablets, or more specific ones, such as the current AR glasses available, are enough to offer solutions that improve many industrial processes; repairing, maintenance, plant control, product line reconfiguration are some examples of these tasks. Many industrial applications already utilise AR-based applications to solve those problems. In this work, we aim to go a little bit further beyond current possibilities that only focus on providing visual guidance. Our main goal is to add a semantic layer for existing AR-based applications, that visually validate worker's actions based on visual interpretation of switches, potentiometers, analog needles or buttons, among others. This semantic layer allows a new level of interaction by adding automatic interpretation of the context that affects the actions of the operator and the display of information of interest in the AR system. We propose and explain the architecture and training of the Convolutional Neural Networks (CNN) used for the semantic layer and its integration in the AR technology.

Keywords

Augmented realityCnnConvolutional neural networksDeep learningIndustrial environmentsIndustrial processsIndustryInteractionMultilayer neural networksPlant controlProduct-linesSemantic layerSemanticsTechnological frameVirtual realityVisual guidanceVisual interpretationVoltage dividers

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 1.38, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-22, the following number of citations:

  • WoS: 5
  • Scopus: 6
  • OpenCitations: 3

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-22:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 33 (PlumX).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Izquierdo Doménech, Juan Jesús) and Last Author (Orta López, Jorge).

the author responsible for correspondence tasks has been Izquierdo Doménech, Juan Jesús.