Automating quality inspection with computer vision techniques is often a very
data-demanding task. Specifically, supervised deep learning requires a large
amount of annotated images for training. In practice, collecting and annotating
such data is not only costly and laborious, but also inefficient, given the
fact that only a few instances may be available for certain defect classes. If
working with video frames can increase the number of these instances, it has a
major disadvantage: the resulting images will be highly correlated with one
another. As a consequence, models trained under such constraints are expected
to be very sensitive to input distribution changes, which may be caused in
practice by changes in the acquisition system (cameras, lights), in the parts
or in the defects aspect. In this work, we demonstrate the use of randomly
generated synthetic training images can help tackle domain instability issues,
making the trained models more robust to contextual changes. We detail both our
synthetic data generation pipeline and our deep learning methodology for
answering these questions.

通过生成合成图像来解决领域不稳定性问题，从而使训练的深度学习模型更加稳健。我们描述了我们的合成数据生成管道和我们的深度学习方法来回答这些问题。