QualitAI - Quality control of industrial products via deep learning on images
At a glance
- Project leader : Prof. Dr. Thilo Stadelmann
- Project team : Mohammadreza Amirian, Dr. Frank-Peter Schilling
- Project budget : CHF 710'000
- Project status : completed
- Funding partner : CTI
- Project partner : BW-TEC AG
- Contact person : Thilo Stadelmann
Description
QualitAI researches and develops a device for automatic quality control of industrial products like e.g. cardial ballon catheters. This will be facilitated through innovation in the area of artificial intelligence (AI), especially by analyzing camera images using so-called deep learning.
Publications
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Amirian, Mohammadreza; Tuggener, Lukas; Chavarriaga, Ricardo; Satyawan, Yvan Putra; Schilling, Frank-Peter; Schwenker, Friedhelm; Stadelmann, Thilo,
2021.
Two to trust : AutoML for safe modelling and interpretable deep learning for robustness [paper].
In:
Postproceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020.
1st TAILOR Workshop on Trustworthy AI at ECAI 2020, Santiago de Compostela, Spain, 29-30 August 2020.
Springer.
Available from: https://doi.org/10.21256/zhaw-22061
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Stadelmann, Thilo; Schilling, Frank-Peter,
2019.
Deep Learning in medizinischer Diagnostik und Qualitätskontrolle.
Netzwoche.
Available from: https://doi.org/10.21256/zhaw-20163
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Amirian, Mohammadreza; Schwenker, Friedhelm; Stadelmann, Thilo,
2018.
Trace and detect adversarial attacks on CNNs using feature response maps [paper].
In:
Artificial Neural Networks in Pattern Recognition.
8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018.
Springer.
pp. 346-358.
Lecture Notes in Computer Science ; 11081.
Available from: https://doi.org/10.1007/978-3-319-99978-4_27