Recognition of Defective Mineral Wool Using Pruned ResNet Models

• URL: http://arxiv.org/abs/2211.00466v1
• Date: Tue, 1 Nov 2022 13:58:02 GMT
• Title: Recognition of Defective Mineral Wool Using Pruned ResNet Models
• Authors: Mehdi Rafiei, Dat Thanh Tran, Alexandros Iosifidis
• Abstract summary: 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. 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.
• Score: 88.24021148516319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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