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Anomaly detection for industrial surface inspection : application in maintenance of aircraft components
Citation Link: https://doi.org/10.15480/882.4353
Publikationstyp
Journal Article
Date Issued
2022-05-26
Sprache
English
Institut
TORE-DOI
Journal
Volume
107
Start Page
246
End Page
251
Citation
Procedia CIRP 107: 246-251 (2022)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Elsevier
Peer Reviewed
true
Surface defects on aircraft landing gear components represent a deviation from a normal state. Visual inspection is a safety-critical, but recurring task with automation aspiration through machine vision. Various rare occurring faults make acquisition of appropriate training data cumbersome, which represents a major challenge for artificial intelligence-based optical inspection. In this paper, we apply an anomaly detection approach based on a convolutional autoencoder for defect detection during inspection to encounter the challenge of lacking and biased training data. Results indicated the potential of this approach to assist the inspector, but improvements are required for a deployment.
Subjects
optical inspectiont
anomaly detection
surface defects
machine vision
DDC Class
600: Technik
620: Ingenieurwissenschaften
Funding Organisations
More Funding Information
Research was funded by the German Federal Ministry for Economics and Climate Action under the Program LuFo V-3.
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