AI in automated scratch-dig inspection (Conference Presentation)

Daniel Kiefhaber; Peter Würtz; Maximilian Bopp; Wolfgang Mischler; Jean-Michel Asfour

doi:10.1117/12.2663764

13 July 2023 AI in automated scratch-dig inspection (Conference Presentation)

Daniel Kiefhaber, Peter Würtz, Maximilian Bopp, Wolfgang Mischler, Jean-Michel Asfour

Proceedings Volume 12524, Dimensional Optical Metrology and Inspection for Practical Applications XII ; 1252403 (2023) https://doi.org/10.1117/12.2663764
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

The tricky part in deploying AI models to production is often training, with two important prerequisites: One, training data must be representative of the data that the algorithm will see in later use. And two, training data must be properly labeled manually. In algorithms for automated optical inspection, there is a further problem: What if there are only a few examples of specific defect types? We tackled these problems with different strategies when developing our ARGOS system for scratch-dig inspection. We will present real-world examples of how AI algorithms can be used for defect detection and classification without large training databases.

Conference Presentation

Citation Download Citation

Daniel Kiefhaber, Peter Würtz, Maximilian Bopp, Wolfgang Mischler, and Jean-Michel Asfour "AI in automated scratch-dig inspection (Conference Presentation)", Proc. SPIE 12524, Dimensional Optical Metrology and Inspection for Practical Applications XII , 1252403 (13 July 2023); https://doi.org/10.1117/12.2663764

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