FabricNET: A Microscopic Image Dataset of Woven Fabrics for Predicting Texture and Weaving Parameters through Machine Learning

This research presents an approach aimed at enhancing texture recognition and weaving parameter estimation in the textile industry to align with sustainability goals and improve product quality. By utilizing low-cost handheld microscopy and machine learning, this method offers the potential for more precise production outcomes. In this study, textile images were manually labeled for texture, specific mass, weft, and warp parameters, followed by the extraction of various texture features, resulting in a comprehensive dataset comprising four hundred and fifty-eight inputs and four outputs. Prominent machine learning algorithms, including XGBoost, RF, and MLP, were applied, resulting in noteworthy achievements. Specifically, XGBoost demonstrated an impressive texture classification accuracy of 0.987, while RF yielded the lowest MAE (5.121 g/cm) in specific mass prediction. Additionally, weft and warp estimations displayed superior accuracy compared to manual measurements. This research emphasizes the crucial role of AI in improving efficiency and sustainability within the textile industry, potentially reducing resource wastage, enhancing worker safety, and increasing productivity. These advancements hold the promise of significant positive environmental and social impacts, marking a substantial step forward in the industry’s pursuit of its objectives.