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Deep-learning-based out-of distribution data detection in visual inspection images
University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing. (KAMPT)
Department of Electrical Engineering, Chalmers University, Gothenburg (SWE).
2023 (English)In: Proceedings Of Spie  12489, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE, 1248909 (25 April 2023) / [ed] Norbert G. Meyendorf; Christopher Niezrecki; Ripi Singh, Spie Digital Library , 2023, Vol. 1248909, p. 1-10Conference paper, Published paper (Refereed)
Abstract [en]

Within quality critical industries, e.g. aerospace, quality control with non-destructive evaluation (NDE) is essential. The surface quality is often important and e.g. visual inspection is often applied. Part of the inspection is the data interpretation, not easily made automatic for critical products. Recent studies on the automatization have indicated promising results utilizing deep-learning-based artificial intelligence. However, many such algorithms are known to be overconfident when subjected to unexpected input (e.g. new/rare material defects) far from the training dataset, so-called out-of-distribution (OOD) data. We claim that safe computer-based interpretation of NDE data within quality critical applications, must respond sensible also to OOD data.

A sensible response could be that the algorithms identify such OOD data and forward it to a human for further analysis. Such an OOD detector could facilitate a human-machine collaboration in a NDE 4.0 vision. In this work we have explored if a recently proposed (for industrial x-ray images) auto-encoder-based approach can be utilized as OOD detector (one-class classifier) for visual inspection data. The model is trained in an unsupervised manner on accepted input to reconstruct it at high precision. Simultaneously it is trained to remove synthetically added defect indications to generate a clean image patch, similar to denoising-auto-enoders. The difference between the input and reconstructed input is analyzed for OOD detection. We train and test the algorithm on a publicly available visual inspection dataset with surface defects. We achieve true positive rates at 0.90 with true negative rates at 0.99 and demonstrate detection of OOD data.

Place, publisher, year, edition, pages
Spie Digital Library , 2023. Vol. 1248909, p. 1-10
Keywords [en]
Visual Inspection, Non-Destructive Evaluation, Deep Learning, NDE Reliability
National Category
Manufacturing, Surface and Joining Technology
Research subject
Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-20150DOI: 10.1117/12.2657240OAI: oai:DiVA.org:hv-20150DiVA, id: diva2:1771855
Conference
SPIE Smart Structures + Nondestructive Evaluation, 2023, Long Beach, California, United States
Available from: 2023-06-21 Created: 2023-06-21 Last updated: 2024-11-21Bibliographically approved

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Lindgren, Erik

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