Proceedings of International Conference on Applied Innovation in IT
2026/03/31, Volume 14, Issue 1, pp.41-47

Machine Learning for Fabric Type Recognition in Sewing Processes


Nasiba Palvannazirova, Aybek Mavlyanov and Petr Butovskiy


Abstract: This scientific article presents an artificial intelligence system for the automatic identification of types of fabric in the garment industry. The system differentiates between six categories of materials: cotton, polyester, silk, wool, denim and mixed fabrics. The development of the system was based on the pre-training neural network VGG16, which was modified to fit the data of textiles. On the other hand, actual production situations were taken into consideration, e.g. variable lighting in workshops, varied material textures. To train the system, a data set of 540 images was created, each of which were created under different illumination and in different angles to improve the reliability of the recognition. The training process consisted of three different stages. First of all, it was necessary to configure the classifier for the particular categories of fabric. Subsequently, further layers of neural network were unfrozen for subsequent further tuning. In the last stage, the entire network went through retraining. Consequently, the accuracy of the recognition achieved 95.8%. The researchers also tested light versions of the system, with different architectures available, on a Raspberry Pi 4 mini computer. This way we were able to determine the best balance between accuracy of recognition and speed of real-time operation. The results showed neural networks are capable of recognising fabrics at an almost human level. At the same time, the system can be optimised to operate on small devices directly in the sewing workshops. Thus work that combines scientific developments, practical applications into industrial use and opens the way to full automation of sewing production.

Keywords: Fabric Recognition, VGG16, Transfer Learning, Edge Computing, Sewing Automation, Raspberry Pi 4, Deep Learning.

DOI: Under indexing

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References:

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