Automated Defect Detection for Non-Destructive Evaluation by Radar Imaging and Machine Learning

Ullmann I, Egerer P, Schür J, Vossiek M (2020)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Event location: Cottbus DE

Abstract

This work provides a concept for a fully automated, non-invasive evaluation of dielectric objects by use of synthetic aperture radar (SAR) imaging combined with machine learning for application in non-destructive evaluation (NDE).

For demonstration purposes we investigated a set of polyvinylchloride (PVC) test objects. A millimetre-wave imaging system is used to scan the objects. By employing subsurface SAR imaging techniques, high-resolution images from the objects’ insides can be obtained. These images are used as a dataset for a convolutional neural network. By training the network on several typical defect structures such as cracks, delaminations or contaminations, the proposed procedure enables an automated decision whether an object under test is intact or defective, independent of the kind of defect.

Authors with CRIS profile

Ingrid Ullmann Lehrstuhl für Hochfrequenztechnik Jan Steffen Schür Lehrstuhl für Hochfrequenztechnik Martin Vossiek Lehrstuhl für Hochfrequenztechnik

How to cite

APA:

Ullmann, I., Egerer, P., Schür, J., & Vossiek, M. (2020). Automated Defect Detection for Non-Destructive Evaluation by Radar Imaging and Machine Learning. In Proceedings of the 13th German Microwave Conference (GeMiC 2020). Cottbus, DE.

MLA:

Ullmann, Ingrid, et al. "Automated Defect Detection for Non-Destructive Evaluation by Radar Imaging and Machine Learning." Proceedings of the 13th German Microwave Conference (GeMiC 2020), Cottbus 2020.

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