Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (06): 91-96.doi: 10.13475/j.fzxb.20201105806

• Textile Engineering • Previous Articles     Next Articles

On-line detection of warp collision and reed embedding based on improved inter-frame difference method

XIA Xuwen, MENG Shuo, PAN Ruru(), GAO Weidong   

  1. Key Laboratory of Eco-Textiles (Jiangnan University), Ministry of Education, Wuxi, Jiangsu 214122, China
  • Received:2020-11-30 Revised:2021-03-14 Online:2021-06-15 Published:2021-06-28
  • Contact: PAN Ruru E-mail:prrsw@163.com

RichHTML

6

PDF (PC)

50

Abstract:

In order to monitor the warp bumping phenomenon in the warp sizing process, a real-time automatic detection system for warp reed collision of the sizing machine based on machine vision was developed. The complete image of warp yarn passing through the reed was captured through the developed image acquisition system. The initial image was cropped to obtain the detection area, and before the image through Gaussian filtering was smoothed to reduce the level of image details. Then, the inter-frame difference was enlarged, and the Canny edge algorithm and expanded mathematical morphology operation were added to complete the improvement of the inter-frame difference method. Recognition and tracking of the warp yarn collision and reed holding of the sizing machine were achieved by the improved inter-frame difference method. The experimental results show that the developed detection system can accurately identify the warp in the sizing machine and track the reed-holding yarn, fulfilling the real-time detection function, which can guide the actual production.

Key words: reed detection, image processing, frame difference method, gaussian filter, morphological operations

CLC Number: 

  • TS111.8

Fig.1

Image acquisition system. (a) Schematic diagram of image acquisition system; (b) Monitoring equipment module"

Fig.2

Part of collected warp images. (a) Warp passes through reed normally; (b) warp yarn collides with reed"

Fig.3

Cropped image"

Fig.4

Image smoothing effect comparison. (a) Grayscale image; (b) Gaussian smooth image"

Fig.5

Schematic diagram of traditional inter-frame difference method"

Fig.6

Schematic diagram of improved inter-frame difference method"

Tab.1

Accuracy of collision reed detection at frame difference and vehicle speed"

车速/(m·min-1) 帧间差t/帧 准确率/%
20 14.40
30 16.52
40 21.34
10 50 24.51
60 28.89
80 37.34
100 40.97
20 30.01
30 40.87
40 53.18
30 50 59.08
60 64.51
80 75.43
100 79.17
20 45.04
30 60.72
40 68.37
50 50 77.07
60 82.17
80 88.68
100 89.75
20 46.63
30 69.08
40 70.54
70 50 84.02
60 87.07
80 90.89
100 91.34

Fig.7

Differential image and binarized image under different frame differences"

Fig.8

Average false detection rate and response time under different threshold δ "

Fig.9

Average response time of system at different vehicle speeds"

[1] 朱苏康, 高卫东. 机织学[M]. 北京: 中国纺织出版社, 2015:75.
ZHU Sukang, GAO Weidong. Weaving science[M]. Beijing: China Textile & Apparel Press, 2015:75.
[2] 陈长虎, 程西楼, 黄彦萍. 细号高密集聚纺织物浆纱工艺探讨[J]. 棉纺织技术, 2015, 43(1):61-64.
CHEN Changhu, CHENG Xilou, HUANG Yanping. Discussion on the sizing process of fine-number and high-density poly-textile[J]. Cotton Textile Technology, 2015, 43(1):61-64.
[3] 常永和, 薛元, 韩晨晨, 等. 环锭纺导纱板受力信号在线检测及纺纱张力分析[J]. 纺织学报, 2018, 39(9):146-152.
CHANG Yonghe, XUE Yuan, HAN Chenchen, et al. On-line detection of force signal of ring spinning guide plate and analysis of spinning tension[J]. Journal of Textile Research, 2018, 39(9):146-152.
[4] CHAN J P, PALMER G S. Machine vision-applications in industry[C]// IEEE Colloquium on Application of Machine Vision. London: IET, 1995:1-6.
[5] 王振亚, 曾黄麟. 一种基于帧间差分和光流技术结合的运动车辆检测和跟踪新算法[J]. 计算机应用与软件, 2012, 29(5):117-120.
WANG Zhenya, ZENG Huanglin. A new algorithm for moving vehicle detection and tracking based on the combination of inter-frame difference and optical flow technology[J]. Computer Applications and Software, 2012, 29(5):117-120.
[6] 苏佳, 高丽慧. 采用帧差法和相关滤波改进的TLD跟踪算法[J]. 计算机工程与设计, 2020, 41(6):1694-1700.
SU Jia, GAO Lihui. Improved TLD tracking algorithm using frame difference method and related filtering[J]. Computer Engineering and Design, 2020, 41(6):1694-1700.
[7] DAI Kexue. Prospects and current studies on back-ground subtraction techniques for moving objects detection from surveillance video[J]. Journal of Image and Graphics, 2006, 11(7):917-919.
[8] SANDFORD A, SARKER T, BERNIER T. Effects of geometric distortions, gaussian blur, and contrast negation on recognition of familiar faces[J]. Visual Cognition, 2017:1-16.
[9] 高海壮, 段先华. 基于帧差法和混合高斯的海上运动目标检测[J]. 计算机与数字工程, 2019, 47(5):1140-1144.
GAO Haizhuang, DUAN Xianhua. Detection of sea moving targets based on frame difference method and Gaussian mixture[J]. Computer and Digital Engineering, 2019, 47(5):1140-1144.
[10] 孙晓杰, 梁义. 基于双阈值的图像边缘实时检测系统[J]. 仪表技术与传感器, 2019(8):83-86.
SUN Xiaojie, LIANG Yi. Real-time image edge detection system based on dual thresholds[J]. Instrumentation Technology and Sensors, 2019(8):83-86.
[11] 李航. 统计学习方法[M]. 北京: 清华大学出版社, 2012:18-19.
LI Hang. Statistical learning methods[M]. Beijing: Tsinghua University Press, 2012:18-19.
[1] JIANG Yanting, YAN Qingshuai, XIN Binjie, GAO Cong, SHI Meiwu. Comparative study on testing methods for unidirectional water transport in fabrics [J]. Journal of Textile Research, 2021, 42(05): 51-58.
[2] LI Dongjie, GUO Shuai, YANG Liu. Yarn defect detection based on improved image threshold segmentation algorithm [J]. Journal of Textile Research, 2021, 42(03): 82-88.
[3] TANG Qianhui, WANG Lei, GAO Weidong. Detection of fabric shape retention based on image processing [J]. Journal of Textile Research, 2021, 42(03): 89-94.
[4] MENG Shuo, XIA Xuwen, PAN Ruru, ZHOU Jian, WANG Lei, GAO Weidong. Detection of fabric density uniformity based on convolutional neural network [J]. Journal of Textile Research, 2021, 42(02): 101-106.
[5] ZHANG Zhengye, XIN Binjie, DENG Na, CHEN Yang, XING Wenyu. Research and application of algorithm for measuring hemp fiber cross-sectional parameters based on boundary tracking [J]. Journal of Textile Research, 2020, 41(02): 39-43.
[6] WU Yilun, LI Zhongjian, PAN Ruru, GAO Weidong, ZHANG Ning. Weft knitted fabric appearance simulation using colored spun yarn image [J]. Journal of Textile Research, 2019, 40(06): 111-116.
[7] HUANG Jiajun, KE Wei, WANG Jing, DENG Zhongmin. Color shading detection and rating system for denim based on computer vision [J]. Journal of Textile Research, 2019, 40(05): 163-169.
[8] WANG Wendi, XIN Binjie, DENG Na, LI Jiaping, LIU Ningjuan. Identification and application of yarn hairiness using adaptive threshold method under single vision [J]. Journal of Textile Research, 2019, 40(05): 150-156.
[9] CAI Yichao, ZHOU Xiao, SONG Mingfeng, MOU Xin'gang. Defect detection of cheese yarn based on multi-scale multi-direction template convolution [J]. Journal of Textile Research, 2019, 40(04): 152-157.
[10] . Detection and evaluation on yarn hairiness of blackboard with image processing [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(08): 144-149.
[11] . Position recognition of spinning yarn breakage based on convolution neural network [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(06): 136-141.
[12] . Detecting method of foreign fibers in seed cotton based on deep-learning [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(06): 131-135.
[13] . Spinning breakage detection based on optimized hough transform [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(04): 36-41.
[14] . Application of algorithm with improved frequency-tuned salient region [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(03): 154-160.
[15] . On-line yarn cone defects detection system based on machine vision [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(01): 139-145.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号
Copyright 2021 © Editorial office of Journal of Textile Research
Supported by Beijing Magtech Co.Ltd