Yarn Density Measurement for 3-D Braided Composite Preforms Based on Rotation Object Detection

Dai, Li; Li, Dawei; Tu, Qifan; Wang, Jun

doi:10.1109/tim.2022.3192294

Cited by 6 publications

(1 citation statement)

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“…The extraction network of a texture’s structure proposed by Yuan et al [ 19 ] extracted periodic texture information from fabric images to accomplish yarn segmentation. Dai et al [ 20 ] designed a network consisting of a dilated feature network and a feature alignment module to detect each segment of the yarn based on rotating object detection. Meng et al [ 21 ] improved the learning of yarn features by detecting yarns and floats in multitask learning.…”

Section: Introductionmentioning

confidence: 99%

Extraction of the Microstructure of Wool Fabrics Based on Structure Tensor

Zhu,

Ma,

Ding

et al. 2023

Sensors

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The trends of “fashionalization”, “personalization” and “customization” of wool fabrics have prompted the textile industry to change the original processing design based on the experience of engineers and trial production. In order to adapt to the promotion of intelligent production, the microstructure of wool fabrics is introduced into the finishing process. This article presents an automated method to extract the microstructure from the micro-CT data of woven wool fabrics. Firstly, image processing was performed on the 3D micro-CT images of the fabric. The raw grayscale data were converted into eigenvectors of the structure tensor to segment the individual yarns. These data were then used to calculate the three parameters of diameter, spacing and the path of the center points of the yarn for the microstructure. The experimental results showed that the proposed method was quite accurate and robust on woven single-ply tweed fabrics.

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Section: Introductionmentioning

confidence: 99%