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Automating quality inspection with computer vision techniques is often a verydata-demanding task. Specifically, supervised deep learning requires a largeamount of annotated images for training. In practice, collecting and annotatingsuch data is not only costly and laborious, but also inefficient, given thefact that only a few instances may be available for certain defect classes. Ifworking with video frames can increase the number of these instances, it has amajor disadvantage: the resulting images will be highly correlated with oneanother. As a consequence, models trained under such constraints are expectedto be very sensitive to input distribution changes, which may be caused inpractice by changes in the acquisition system (cameras, lights), in the partsor in the defects aspect. In this work, we demonstrate the use of randomlygenerated synthetic training images can help tackle domain instability issues,making the trained models more robust to contextual changes. We detail both oursynthetic data generation pipeline and our deep learning methodology foranswering these questions.

Improving generalization with synthetic training data for deep learning based quality inspection