Adaptive Reference Image Set Selection in Automated X-Ray Inspection

Abstract

The automatic radioscopic inspection of industrial parts usually uses reference based methods. These methods select, as benchmark for comparison, image data from good parts to detect the anomalies of parts under inspection. However, parts can vary within the specification during the production process, which makes comparison of older reference image sets with current images of parts difficult and increases the probability of false rejections. To counter this variability, the reference image sets have to be updated. This paper proposes an adaptive reference image set selection procedure to be used in the assisted defect recognition (ADR) system in turbine blade inspection. The procedure first selects an initial reference image set using an approach called ADR Model Optimizer and then uses positive rate in a sliding-time window to determine the need to update the reference image set. Whenever there is a need, the ADR Model Optimizer is retrained with new data consisting of the old reference image sets augmented with false rejected images to generate a new reference image set. The experimental result demonstrates that the proposed procedure can adaptively select a reference image set, leading to an inspection process with a high true positive rate and a low false positive rate.