Journal of Textile Research ›› 2026, Vol. 47 ›› Issue (04): 136-144.doi: 10.13475/j.fzxb.20250706701

• Textile Engineering • Previous Articles     Next Articles

Online detection of sized yarn hairiness based on machine vision

HUANG Yuxiang, PAN Xinming, GUO Mingrui, WANG Jing'an, GAO Weidong()   

  1. College of Textile Science and Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Received:2025-07-19 Revised:2026-01-08 Online:2026-04-15 Published:2026-06-24
  • Contact: GAO Weidong E-mail:gaowd@163.com

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Abstract:

Objective Sizing is a key step in weaving preparation. It effectively controls yarn hairiness, improves weaving efficiency, and enhances fabric quality. However, hairiness detection during sizing still relies on offline methods. These methods fail to provide timely feedback on hairiness levels and hinder precise process control. To address this issue, we proposed a machine vision-based method for online detection of sized yarn hairiness. This method is expected to enable real-time hairiness monitoring during the sizing process.

Method An online image acquisition system was built using an industrial camera, a laser light source, and a motorized screw rod. After autofocus, high-resolution images of the sized yarn sheet were continuously captured. The acquired images were then processed by a deep-learning-based segmentation model to generate binary hairiness images. A thresholding algorithm was applied to extract binary yarn images. Edge pixels in both the hairiness and yarn stem images were counted separately. The hairiness ratio (Hr) was calculated as the ratio of edge pixels in the hairiness binary image to those in the yarn core binary image, serving as an indicator for evaluating the sized yarn hairiness level.

Results To verify the effectiveness of the proposed method under various influencing factors, an experimental device was set up and used to simulate the actual yarn running conditions in the sizing process. A variety of yarn samples with different linear densities, fiber types, colors, and construction type were selected to ensure the validity and universality of proposed method. The detection results (Hr) were compared with the H index measured by the Uster Tester 5. The detection results showed that the proposed method achieved the a correlation coefficient of 0.94 between Hr and the H index, outperforming the UNet model's 0.91 and the traditional Canny algorithm's 0.83. In terms of efficiency, both the proposed method and the UNet model, as deep learning-based approaches, were able to fully utilize the parallel computing capabilities GPU, and the number of images processed per second was significantly higher than the 2.87 frames per second of the Canny algorithm. Benefiting from lightweight design, the proposed method achieved a processing speed of over 10 frames per second, which is superior to the 7.31 frames per second of UNet, indicateing improved detection accuracy, real-time performance, and variety adaptability. To assess the robustness and operational stability of the proposed method under dynamic production conditions, a speed adaptability test was conducted. Hr values were measured at yarn running speeds of 70, 90, and 110 m/min and compared with baseline results at 50 m/min. Test results showed that Hr initially decreased with increasing speed but rose again at higher speeds. This trend is attributed to the edge diffusion effect, where blurred or diffused hairiness boundaries increase the number of detected edge pixels, raising the Hr value. Despite the influence of blur caused by motion, deviations in detection results across all tested speeds remained within 5%. In terms of environmental adaptability, the detection result of the proposed method at illumination levels above level 7 was only 5% compared to level 10 brightness. This proves that the method can adapt to the real-time detection of hairiness under complex conditions in sizing production.

Conclusion This study developed an online hairiness detection system on sizing machine, including an image acquisition device, a deep learning segmentation model, and a new index (Hr) to characterize the hairiness. The results showed that the Hr and the H index had a high level of correlation, which confirms the effectiveness of the proposed method. The experiments demonstrated that the proposed method had desirable effectiveness and real-time efficiency, as well as sufficient adaptability under varying speeds, illumination conditions, and yarn properties. During the sizing process, the method can record and plot the variation curve of hairiness, allowing real-time monitoring and data archiving. The hairiness detection results also reflect the influence of process parameters in real time and provides historical data to support process optimization.

Key words: sized yarn hairiness, machine vision, online detection, image segmentation, hairiness evaluation index

CLC Number: 

  • TS103.7

Fig.1

Online image acquisition device"

Fig.2

Comparison of image before(a) and after(b) autofocusing"

Fig.3

Lightweight multiscale high-frequency informative attention image segmentation model"

Fig.4

Yarn hairiness image (a)and segmentation result(b)"

Fig.5

Schematic diagram of edge enhancement module structure"

Fig.6

Schematic diagram of multi-scale high-frequency information attention module structure"

Fig.7

Schematic diagram of a depthwise separable convolution structure"

Fig.8

Binary image of yarn main body, segmentation result of fuzz and edge extraction result. (a) Hairiness segmentation result; (b) Extracted hairiness edge; (c) Binarized yarn core image; (d) Extracted yarn core edge"

Fig.9

Simulation experiment device"

Tab.1

Samples of yarns with different properties and their H index"

纱线样
本编号		 纱线属性 H
指数
颜色 原料 原纱/
浆纱		 线密
度/tex
1-1 白色 棉 原纱 14.6 4.33
1-2 白色 棉 浆纱 14.6 2.52
1-3 白色 棉 浆纱 14.6 2.03
1-4 白色 棉 浆纱 14.6 1.89
1-5 白色 棉 浆纱 14.6 1.63
2-1 白色 棉 原纱 27.8 6.81
2-2 白色 棉 浆纱 27.8 2.97
3-1 白色 涤纶/棉(60/40) 原纱 13.0 2.90
3-2 白色 涤纶/棉(60/40) 浆纱 13.0 1.87
4-1 白色 粘胶 原纱 14.6 3.70
4-2 白色 粘胶 浆纱 14.6 2.17
5-1 白色 涤纶 原纱 14.6 6.82
5-2 白色 涤纶 浆纱 14.6 5.36
6-1 白色 腈纶 原纱 19.7 8.39
6-2 白色 腈纶 浆纱 19.7 5.80
7-1 白色 锦纶 原纱 14.6 5.19
7-2 白色 锦纶 浆纱 14.6 2.95
8-1 蓝色 棉 原纱 14.8×2 7.46
8-2 蓝色 棉 浆纱 14.8×2 3.27
9-1 绿色 棉 原纱 18.2×2 7.10
9-2 绿色 棉 浆纱 18.2×2 3.34
10-1 咖啡 棉 原纱 29.2×2 9.23
10-2 咖啡 棉 浆纱 29.2×2 4.23

Tab.2

Performance of model in feather segmentation under different Gaussian blur scale parameters"

k1 δ1 k2 δ2 TIoU
3 0.8 7 1.8 0.79
5 1.0 7 1.8 0.80
5 1.0 9 2.4 0.80
7 1.8 11 2.8 0.82
9 2.4 11 2.4 0.80
无高斯模糊 0.78

Tab.3

Hr,H index and their correlation coefficients obtained by image processing methods"

纱线样
本编号		 H
指数		 Hr
LFMHNet UNet ERFNet LV_UNet Canny
1-1 4.33 2.39 2.65 2.39 2.46 2.59
1-2 2.52 0.54 0.68 0.66 0.89 0.82
1-3 2.03 0.24 0.35 0.62 0.56 0.52
1-4 1.89 0.47 0.45 0.65 0.64 0.75
1-5 1.63 0.34 0.41 0.31 0.59 0.75
2-1 6.81 3.65 3.55 2.95 3.81 3.22
2-2 2.97 0.80 0.92 1.14 1.26 1.16
3-1 2.90 1.92 1.86 1.31 1.11 2.38
3-2 1.87 0.45 0.62 0.53 0.95 0.77
4-1 3.70 1.74 1.64 1.96 1.91 2.21
4-2 2.17 0.78 0.56 1.32 1.21 1.23
5-1 6.82 3.08 2.34 2.84 2.82 2.34
5-2 5.36 2.35 1.35 2.64 2.99 2.75
6-1 8.39 3.35 3.60 2.93 2.88 2.60
6-2 5.80 1.57 1.45 1.53 1.60 1.45
7-1 5.19 2.17 2.54 2.22 2.17 3.17
7-2 2.95 1.21 0.69 1.54 1.87 0.69
8-1 7.46 3.17 3.25 3.43 3.29 3.61
8-2 3.27 0.45 0.65 0.79 0.51 0.55
9-1 7.10 3.50 3.65 3.98 3.68 3.91
9-2 3.34 0.67 0.64 0.42 0.48 0.46
10-1 9.23 4.88 4.88 4.15 4.62 4.13
10-2 4.23 1.13 0.79 0.76 0.71 0.85
相关系数 0.94 0.91 0.90 0.89 0.83

Fig.10

Scatter plot of linear regression between Hr(LFMHNet) and H index"

Tab.4

Processing efficiency of different image processing methods"

图像处理方法 图像尺寸/像素 图像处理速度/(帧·s-1)
LFMHNet 2 448×2 048 10.12
UNet 2 448×2 048 7.31
ERFNet 2 448×2 048 6.75
LV_UNet 2 448×2 048 5.32
Canny 2 448×2 048 2.87

Tab.5

Detection results and MAPE at different speeds"

车速/(m·min-1) Hr 平均绝对百分比误差/%
50 2.39 0
70 2.37 0.84
90 2.32 2.93
110 2.50 4.60

Tab.6

MAPE of detection results at different sampling frequencies"

帧率/
(帧·s-1)		 平均绝对百分比误差/%
样品1-1 样品1-3
10 0 0
5 0.21 0.81
2 0.15 1.92
1 1.84 3.43

Tab.7

Detection results and MAPE at different brightness levels"

亮度等级 Hr 平均绝对百分比误差/%
10 2.39 0.00
9 2.42 1.26
8 2.44 2.09
7 2.58 7.95
6 3.80 56.00

Fig.11

Images of yarn hairiness at brightness levels 10(a), 9(b), 8(c), 7(d) and 6(e)"

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