Recognition of Defective Mineral Wool Using Pruned ResNet Models

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Abstract

Mineral wool production is a non-linear process that makes it hard to control the final quality. Therefore, having a nondestructive 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.
OriginalsprogEngelsk
Titel2023 IEEE 21st International Conference on Industrial Informatics (INDIN)
RedaktørerHelene Dorksen, Stefano Scanzio, Jurgen Jasperneite, Lukasz Wisniewski, Kim Fung Man, Thilo Sauter, Lucia Seno, Henning Trsek, Valeriy Vyatkin
ForlagIEEE
Publikationsdatojul. 2023
ISBN (Elektronisk)978-1-6654-9313-0, 978-1-6654-9314-7
DOI
StatusUdgivet - jul. 2023
NavnIEEE International Conference on Industrial Informatics Proceedings
ISSN2378-363X

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