Anomaly detection for industrial surface inspection: application in maintenance of aircraft components

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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.