IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-662-61794-6_4.html
   My bibliography  Save this book chapter

Herausforderungen und Potenziale von KI-gestützter visueller Inspektion in der Elektronikindustrie

In: Künstliche Intelligenz

Author

Listed:
  • Timo Koppe

    (TU Darmstadt)

  • Jonas Schatz

    (Heraeus)

  • Thomas Hornung

    (Heraeus)

Abstract

Zusammenfassung In den vergangenen Jahren war die Künstliche Intelligenz (KI) Gegenstand intensiver Forschung. Eines der Gebiete, auf dem die Wissenschaft große Erfolge erzielen konnte, ist Computer Vision (Prince 2012). Hierbei lernen Computer zu „sehen“, indem sie visuelle Daten wie Bilder oder Videos analysieren, um Entscheidungen zu treffen oder Erkenntnisse über ihre Umwelt zu gewinnen. Insbesondere dank der Fortschritte im Bereich des Deep Learning konnten Neuronale Netze zuletzt bemerkenswerte Erfolge bei wichtigen Benchmarks in der Forschung erzielen (Krizhevsky et al. 2012; Yin et al. 2019), wobei sie auch deutlich effizienter wurden (Tan und Le 2019).

Suggested Citation

  • Timo Koppe & Jonas Schatz & Thomas Hornung, 2021. "Herausforderungen und Potenziale von KI-gestützter visueller Inspektion in der Elektronikindustrie," Springer Books, in: Peter Buxmann & Holger Schmidt (ed.), Künstliche Intelligenz, edition 2, chapter 4, pages 65-80, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-61794-6_4
    DOI: 10.1007/978-3-662-61794-6_4
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-662-61794-6_4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.