Automated processing of X-ray computed tomography images via panoptic segmentation for modeling woven composite textiles

TL;DR

This paper introduces a novel machine learning approach using panoptic segmentation to automatically generate detailed 3D models of woven composite textiles from X-ray CT images, enabling improved analysis of textile structures.

Contribution

It presents the first deep learning-based automated segmentation method for individual yarns in woven textiles, enhancing accuracy on low contrast CT datasets and introducing a new universal evaluation metric.

Findings

The segmentation network generalizes well to similar CT images.

The approach captures yarn flow, contact regions, and cross-sectional variations.

It does not extrapolate well to different geometries or textures.

Abstract

A new, machine learning-based approach for automatically generating 3D digital geometries of woven composite textiles is proposed to overcome the limitations of existing analytical descriptions and segmentation methods. In this approach, panoptic segmentation is leveraged to produce instance segmented semantic masks from X-ray computed tomography (CT) images. This effort represents the first deep learning based automated process for segmenting unique yarn instances in a woven composite textile. Furthermore, it improves on existing methods by providing instance-level segmentation on low contrast CT datasets. Frame-to-frame instance tracking is accomplished via an intersection-over-union (IoU) approach adopted from video panoptic segmentation for assembling a 3D geometric model. A corrective recognition algorithm is developed to improve the recognition quality (RQ). The panoptic quality (PQ) metric is adopted to provide a new universal evaluation metric for reconstructed woven composite textiles. It is found that the panoptic segmentation network generalizes well to new CT images that are similar to the training set but does not extrapolate well to CT images of differing geometry, texture, and contrast. The utility of this approach is demonstrated by capturing yarn flow directions, contact regions between individual yarns, and the spatially varying cross-sectional areas of the yarns.