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YOLO-Based Object Detection in Industry 4.0 Fischertechnik Model Environment

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Intelligent Systems and Applications (IntelliSys 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 823))

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Abstract

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.

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Author information

Authors and Affiliations

  1. BTU Cottbus-Senftenberg, Chair of Automation technology, Platz der Deutschen Einheit 1, 03046, Cottbus, Germany

    Slavomira Schneidereit & Marc Gebauer

  2. Institute for Mathematics, BTU Cottbus-Senftenberg, Platz der Deutschen Einheit 1, 03046, Cottbus, Germany

    Ashkan Mansouri Yarahmadi, Toni Schneidereit & Michael Breuß

Authors
  1. Slavomira Schneidereit
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  2. Ashkan Mansouri Yarahmadi
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  3. Toni Schneidereit
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  4. Michael Breuß
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  5. Marc Gebauer
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Corresponding author

Correspondence to Ashkan Mansouri Yarahmadi .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Schneidereit, S., Yarahmadi, A.M., Schneidereit, T., Breuß, M., Gebauer, M. (2024). YOLO-Based Object Detection in Industry 4.0 Fischertechnik Model Environment. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-031-47724-9_1

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