Procedia CIRP · 2022 · 23 citations · 10 references
ComputingComputer EngineeringComputer ScienceComputer VisionMachine LearningMachine VisionAnomaly DetectionDeep LearningDefect DetectionFault DetectionIndustrial InspectionInspectionAutoencoder-based Anomaly DetectionComputer Vision For InspectionConvolutional Neural Networks For InspectionDeep Learning For InspectionDeep Learning-based Anomaly DetectionFault Detection Using Deep LearningSurface InspectionVisual Inspection
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.
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