Deep-Learning-Based Determination of Textile Properties: A Novel Triplet Architecture Approach for Classifying Cotton Content

To reach sustainable production and consumption patterns, recycling is a key task. Automating the recycling process, especially in sorting tasks, is a strong hope to improve the efficiency and economic viability of the recycling industry. Especially the determination of textile properties such as cotton content is an important task in recycling and the textile industry as a whole. Approaches using NIR-spectrography are common, but can be costly and produce complex datasets. We therefore propose a visual approach to classify fabrics after their cotton content. For this, we apply a novel triplet-architecture approach with a variety of modifications. We combine ResNet50, EfficientNetB4, and DenseNet121 in this architecture in order to make use of their respective strengths while overcoming the weaknesses of the models. For a further accuracy enhancement, we modified all three architectures with adaptive feature pyramid networks, and we added a deformable convolution layer to ResNet50 and DenseNet121. Additionally, we use a second fully connected layer to enhance the model’s classification capacity. With this architecture approach, we achieve an average Root Mean Squared Error of 14.77%, setting a new benchmark for visual approaches to cotton-content classification using cross-validation. We further prove the effectiveness and enhanced accuracy of using a triplet model approach, as well as using all of our modifications. Visual approaches are not market-ready yet, but we show the potential of deep-learning methods for lowering labor-intensiveness and time needed in textile property determination. By that, the recycling industry can be made more efficient and economically viable, helping to come closer to circular economy practices in the textile industry.