Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset
In recent years, the industrial sector has evolved towards its fourth
revolution. The quality control domain is particularly interested in advanced
machine learning for computer vision anomaly detection. Nevertheless, several
challenges have to be faced, including imbalanced datasets, the image
complexity, and the zero-false-negative (ZFN) constraint to guarantee the
high-quality requirement. This paper illustrates a use case for an industrial
partner, where Printed Circuit Board Assembly (PCBA) images are first
reconstructed with a Vector Quantized Generative Adversarial Network (VQGAN)
trained on normal products. Then, several multi-level metrics are extracted on
a few normal and abnormal images, highlighting anomalies through reconstruction
differences. Finally, a classifer is trained to build a composite anomaly score
thanks to the metrics extracted. This three-step approach is performed on the
public MVTec-AD datasets and on the partner PCBA dataset, where it achieves a
regular accuracy of 95.69% and 87.93% under the ZFN constraint.