Recognition of Defective Mineral Wool Using Pruned ResNet Models
This work provides an incremental improvement for mineral wool manufacturers by offering a non-destructive, automated quality control system to enhance defect detection efficiency.
The authors tackled the problem of recognizing defective mineral wool products by developing a visual quality control system using X-ray images and ResNet-based models, achieving over 98% accuracy and identifying 20% more defective products compared to existing methods.
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.