  Quality control is a crucial activity performed by manufacturing companies to
ensure their products conform to the requirements and specifications. The
introduction of artificial intelligence models enables to automate the visual
quality inspection, speeding up the inspection process and ensuring all
products are evaluated under the same criteria. In this research, we compare
supervised and unsupervised defect detection techniques and explore data
augmentation techniques to mitigate the data imbalance in the context of
automated visual inspection. Furthermore, we use Generative Adversarial
Networks for data augmentation to enhance the classifiers' discriminative
performance. Our results show that state-of-the-art unsupervised defect
detection does not match the performance of supervised models but can be used
to reduce the labeling workload by more than 50%. Furthermore, the best
classification performance was achieved considering GAN-based data generation
with AUC ROC scores equal to or higher than 0,9898, even when increasing the
dataset imbalance by leaving only 25\% of the images denoting defective
products. We performed the research with real-world data provided by Philips
Consumer Lifestyle BV.
