Unsupervised anomaly detection for industrial manufacturing using multiple perspectives of free falling parts

We present an extension of our automatic anomaly detection approach for the quality inspection of industrially manufactured parts. The sample under test is imaged from different perspectives simultaneously while it is in free fall to reduce inspection time and minimize part handling. Despite using a diffuse reflecting hollow sphere to achieve the best possible conditions for all camera perspectives, small artifacts from reflections on highly reflecting test specimens and drop shadows appear in the images. The presence of these artifacts leads to the appearance of type I errors. To address this issue, the state-of-the-art for anomaly detection PatchCore1 is modified to handle multiple perspectives at first. Second, a weighting step is added to the image evaluation pipeline. For this, the pose of the test sample is estimated, which is subsequently used to calculate a weight matrix per image. The weights correspond to the local viewing angle of the camera on the sample’s surface because the artifacts occur mainly at steep viewing angles. In addition, two datasets are created to evaluate the proposed approach containing sample data with single and multiple perspectives. The results show that the developed pipeline outperforms PatchCore and the original free-fall inspection setup algorithm. It reaches 95.9% AUROC for Object one and 85.7% AUROC for Object two on validation of multi-perspective datasets. Moreover, combining the proposed approach and the free-fall inspection algorithm improves the results for Object two, achieving 98% AUROC. The conducted experiments allow us to conclude that this approach has the potential to further increase robustness toward various anomalies and artifacts.