Deep Learning Approaches for Automatic Recognition of Textile Weave Structures

The Fabric weave structure is an essential component in the design and manufacture of premium fabric. The structure or pattern used in the fabric's weaving significantly impacts its overall appearance, texture, durability, and drape. Traditional methods of Weave pattern recognition are relied heavily on manual visual inspection, which is time-consuming, prone to human error, struggles with complex patterns and sensitive to lighting conditions Therefore, an automated system is necessary for the classification of woven cloth to improve production efficiency. In this paper we proposed a deep learning model particularly Transfer Learning model that employs data augmentation and transfer learning methods for the classification and identification of woven textiles and compared various also compared MobileNet V2 model with ResNet 50. The model use a MobileNet-V2 to autonomously extract and categorize fabric texture features in an end-to-end fashion. The model is trained using a custom dataset with various weave types, including plain, twill and satin. The experimental findings indicate that the suggested model is resilient and attains cutting-edge precision. We compared our findings with other baseline methods, demonstrating that the suggested technique attained superior accuracy when accounting for rotational orientations in fabric and appropriate illumination conditions.

Deep learning, MobileNet-V2, Pattern recognition, ResNet-50, Transfer learning, Woven fabric

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