@inproceedings{59b7be94f77c413ea3218a3b8d5a678d,
title = "Recognition of Defective Mineral Wool Using Pruned ResNet Models",
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.",
keywords = "Computer vision, Defect recognition, Industrial wool, X-ray",
author = "Mehdi Rafiei and Tran, {Dat Thanh} and Alexandros Iosifidis",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; IEEE International Conference on Industrial Informatics ; Conference date: 17-07-2023 Through 20-07-2023",
year = "2023",
doi = "10.1109/INDIN51400.2023.10217993",
language = "English",
series = "IEEE International Conference on Industrial Informatics (INDIN)",
publisher = "IEEE",
editor = "Helene Dorksen and Stefano Scanzio and Jurgen Jasperneite and Lukasz Wisniewski and Man, {Kim Fung} and Thilo Sauter and Lucia Seno and Henning Trsek and Valeriy Vyatkin",
booktitle = "2023 IEEE 21st International Conference on Industrial Informatics, INDIN 2023",
address = "United States",
}