YOLO-based Object Detection in Industry 4.0 Fischertechnik Model Environment Article Swipe
Slavomira Schneidereit
,
Ashkan Mansouri Yarahmadi
,
Toni Schneidereit
,
Michael Breuß
,
Marc Gebauer
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2301.12827
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2301.12827
In this paper we extensively explore the suitability of YOLO architectures to monitor the process flow across a Fischertechnik industry 4.0 application. Specifically, different YOLO architectures in terms of size and complexity design along with different prior-shapes assignment strategies are adopted. To simulate the real world factory environment, we prepared a rich dataset augmented with different distortions that highly enhance and in some cases degrade our image qualities. The degradation is performed to account for environmental variations and enhancements opt to compensate the color correlations that we face while preparing our dataset. The analysis of our conducted experiments shows the effectiveness of the presented approach evaluated using different measures along with the training and validation strategies that we tailored to tackle the unavoidable color correlations that the problem at hand inherits by nature.
Related Topics
Concepts
Computer science
Factory (object-oriented programming)
Process (computing)
Artificial intelligence
Object (grammar)
Face (sociological concept)
Object detection
Machine learning
Computer vision
Industrial engineering
Pattern recognition (psychology)
Engineering
Social science
Sociology
Operating system
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2301.12827
- https://arxiv.org/pdf/2301.12827
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4318719217
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4318719217Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2301.12827Digital Object Identifier
- Title
-
YOLO-based Object Detection in Industry 4.0 Fischertechnik Model EnvironmentWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-30Full publication date if available
- Authors
-
Slavomira Schneidereit, Ashkan Mansouri Yarahmadi, Toni Schneidereit, Michael Breuß, Marc GebauerList of authors in order
- Landing page
-
https://arxiv.org/abs/2301.12827Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2301.12827Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2301.12827Direct OA link when available
- Concepts
-
Computer science, Factory (object-oriented programming), Process (computing), Artificial intelligence, Object (grammar), Face (sociological concept), Object detection, Machine learning, Computer vision, Industrial engineering, Pattern recognition (psychology), Engineering, Social science, Sociology, Operating system, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |