Automated defect detection in nanomaterial-coated-fabrics using variational autoencoder

Abstract

This paper introduces an unsupervised method for detecting regions with a high density of nanomaterials on coated fabric using Variational Autoencoder, a generative model capable of learning dominant features of the input data and generating similar outputs. The model was applied to images of cotton fabric coated with commercial single-walled carbon nanotubes (SW-CNT) to learn their features. Morphological and electrical properties of the coated samples were initially investigated to identify high-density particle regions. Subsequently, an experiment evaluated the performance of these samples in a specific smart textile application, establishing ground truth for defect localization in each fabric image. Image post-processing techniques were then employed to accurately detect defective regions in test images. The proposed method achieved a recognition rate of 93.2% and the highest Intersection over Union (IoU) of 0.923. This study demonstrates a promising approach for defect identification in coated fabrics, advancing smart textile technology.