  Mineral wool production is a non-linear process that makes it hard to control
the final quality. Therefore, having a non-destructive method to analyze the
product quality and recognize defective products is critical. For this purpose,
we developed a visual quality control system for mineral wool. X-ray images of
wool specimens were collected to create a training set of defective and
non-defective samples. Afterward, we developed several recognition models based
on the ResNet architecture to find the most efficient model. In order to have a
light-weight and fast inference model for real-life applicability, two
structural pruning methods are applied to the classifiers. Considering the low
quantity of the dataset, cross-validation and augmentation methods are used
during the training. As a result, we obtained a model with more than 98%
accuracy, which in comparison to the current procedure used at the company, it
can recognize 20% more defective products.
