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Quality control is a crucial activity performed by manufacturing companies toensure their products conform to the requirements and specifications. Theintroduction of artificial intelligence models enables to automate the visualquality inspection, speeding up the inspection process and ensuring allproducts are evaluated under the same criteria. In this research, we comparesupervised and unsupervised defect detection techniques and explore dataaugmentation techniques to mitigate the data imbalance in the context ofautomated visual inspection. Furthermore, we use Generative AdversarialNetworks for data augmentation to enhance the classifiers' discriminativeperformance. Our results show that state-of-the-art unsupervised defectdetection does not match the performance of supervised models but can be usedto reduce the labeling workload by more than 50%. Furthermore, the bestclassification performance was achieved considering GAN-based data generationwith AUC ROC scores equal to or higher than 0,9898, even when increasing thedataset imbalance by leaving only 25\% of the images denoting defectiveproducts. We performed the research with real-world data provided by PhilipsConsumer Lifestyle BV.

Synthetic Data Augmentation Using GAN For Improved Automated Visual Inspection