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NDE 4.0: Image and Sound Recognition

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Handbook of Nondestructive Evaluation 4.0

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Abstract

Advances powered by Artificial Intelligence (AI) centric technologies have enveloped nearly every aspect of our lives. Of the many aspects of AI, seven patterns have been classified, with the most common being the recognition pattern (Walch, Kathleen-Contributor, Cognitive Word-Contributor Group. September 17, 2019. The seven patterns of AI. https://www.forbes.com/sites/cognitiveworld/2019/09/17/the-seven-patterns-of-ai/?sh=71056b2b12d0). This chapter focuses on pattern recognition with a subset emphasis on image and sound as it may relate to NDE 4.0.

Optical character recognition (OCR) leveraged image recognition for the past decade in document conversion and computer-assisted check deposit may present precedence for AI-assisted flaw detection systems for radiographic images. Computer vision (CV) is the base building block for extraction of data from an image and can recognize objects using algorithms and machine learning concepts (Brownlee, Jason. May 22, 2019 (updated January 27, 2021). Deep Learning for Computer Vision. A Gentle Introduction to Object Recognition with Deep Learning. https://machinelearningmastery.com/object-recognition-with-deep-learning/). Computer vision has been integral in detection, segmentation, classification, monitoring, and prediction of radiographs in the medical community and has applicability in visual and radiographic inspection in industry and the NDE community.

Sound recognition has a large portion of defect formations and flaw mechanical movements release energy in the method of elastic waves with the broad frequency spectrum. Typically, these signals are digitized and converted into amplitude time series. Regardless of their frequency content, these digital acoustics, or sound, signals can be analyzed and classified by any method that applies to time series data, including those developed specifically for audible sound signals such as deep learning algorithms.

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Author information

Authors and Affiliations

  1. Valkim Technologies, San Antonio, TX, USA

    Kimberley Hayes

  2. XaasLabs Inc, Sunnyvale, California, USA

    Amit Rajput

Authors
  1. Kimberley Hayes
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  2. Amit Rajput
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Editor information

Editors and Affiliations

  1. University of Dayton, Dayton, OH, USA

    Norbert Meyendorf

  2. The University of Akron, Akron, OH, USA

    Nathan Ida

  3. Inspiring Next, Cromwell, CT, USA

    Ripi Singh

  4. Vrana GmbH, München, Germany

    Johannes Vrana

Section Editor information

  1. Chemical Materials and Bio Engineering, University of Dayton, Dayton, OH, USA

    Norbert Meyendorf

  2. Department of Electrical and Computer Engineering, The University of Akron, Akron, OH, USA

    Nathan Ida

  3. Inspiring Next, Plus4Pi LLC, Cromwell, CT, USA

    Ripudaman (Ripi) Singh

  4. Vrana GmbH, Rimsting, Deutschland

    Johannes Vrana

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Hayes, K., Rajput, A. (2021). NDE 4.0: Image and Sound Recognition. In: Meyendorf, N., Ida, N., Singh, R., Vrana, J. (eds) Handbook of Nondestructive Evaluation 4.0. Springer, Cham. https://doi.org/10.1007/978-3-030-48200-8_26-1

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  • DOI: https://doi.org/10.1007/978-3-030-48200-8_26-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48200-8

  • Online ISBN: 978-3-030-48200-8

  • eBook Packages: Springer Reference Chemistry and Mat. ScienceReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics

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