Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (10): 176-182.doi: 10.13475/j.fzxb.20210101107

• Machinery & Accessories • Previous Articles     Next Articles

Needle selector detection system based on image processing

YUAN Yanhong1(), ZENG Hongming1, MAO Muquan2   

  1. 1. Key Laboratory of Modern Textile Machinery Technology of Zhejiang Province, Zhejiang Sci-Tech University,Hangzhou, Zhejiang 310018, China
    2. Hangzhou Golden Mechanical and Electronic Technology Co., Ltd.,Hangzhou, Zhejiang 310018, China
  • Received:2021-01-07 Revised:2022-05-19 Online:2022-10-15 Published:2022-10-28

RichHTML

9

PDF (PC)

94

Abstract:

In order to improve the inspection efficiency for needle selectors, a set of automatic needle selector inspection system based on image processing was designed. The system was composed of an ordinary industrial camera, a needle selector controller, a needle selector placement platform and a personal computer. The needle selector is checked by the gray value of the knife head at the two extreme positions. By analyzing the swing rule of the needle selector knife head, the feasibility of using the gray value of the needle selector knife head to judge the knife head swing operation was verified. The Python software was adopted to achieve image acquisition and cropping, and gray value of each cutter head was efficiently extracted by combine Otsu, contour detection, image erosion and other algorithms. The results were compared with the pre-solved threshold to achieve normal operation of the cutter head measurement and judgment. The designed human-computer interaction interface can display the inspection status in real time, and it saves the pictures of the error frames of the needle selector in the designated directory and the error log in the designated file for reference and analysis. The completed system has low cost and no special experimental environment requirements. The actual test of the needle selector that simulates error indicates that the inspection system can effectively realize the detection of the needle selector blade.

Key words: needle selector, trouble shooting, image processing, grayscale value, global shutter, circular weft knitling machine

CLC Number: 

  • TS103.1

Fig.1

Schematic diagram of needle selector"

Fig.2

Swing timing diagram of needle selector in laboratory (needle selection frequency of 100 Hz)"

Fig.3

Schematic diagram of test system"

Fig.4

Overall flow chart of software part of system"

Fig.5

Diagram of camera sensor exposure control in global mode"

Fig.6

Processing process of cutter head analysis area. (a)Needle selector housing analysis box;(b)Minimum circumscribed rectangle;(c)Cutter head analysis area"

Fig.7

Diagram of cutter head after binaried"

Fig.8

Interactive interface"

Fig.9

Needle selector head swings normally"

Fig.10

Swing failure of needle selector"

Tab.1

Gray value of needle selector head under 200 ms exposure time"

类型 左1 右1 左2 右2 左3 右3 左4 右4 左5 右5 左6 右6 左7 右7 左8 右8
背景值 19 20 19 22 24 20 16 19 24 23 15 18 23 24 16 16
刀头左极限位,不摆动 191 0 169 0 182 0 189 0 184 0 199 0 207 0 209 0
刀头右极限位,不摆动 0 191 0 174 0 182 0 194 0 189 0 190 0 194 0 209
摆动频率10 Hz 103 102 93 94 101 99 101 103 102 105 106 103 112 107 111 111
摆动频率20 Hz 102 100 90 94 99 97 99 103 99 102 104 101 110 106 108 109
摆动频率40 Hz 98 97 87 91 96 95 95 99 96 102 100 100 105 106 102 108
摆动频率100 Hz 94 92 83 87 89 93 91 96 90 99 95 97 100 107 95 96
[1] 李红, 袁嫣红, 项宏年. 基于Ansoft Maxwell的电磁选针器瞬态特性分析[J]. 浙江理工大学学报(自然科学版), 2017, 37(1): 92-98.
LI Hong, YUAN Yanhong, XIANG Hongnian. Analysis of transient characteristics of electromagnetic needle selector based on Ansoft Maxwell[J]. Journal of Zhejiang Sci-Tech University ( Natural Sciences Edition), 2017, 37(1): 92-98.
[2] 彭来湖, 王罗俊, 胡旭东, 等. 磁保持电子选针器及串行总线提花系统设计[J]. 纺织学报, 2019, 40(1): 136-141.
PENG Laihu, WANG Luojun, HU Xudong, et al. Magnetic holding electronic needle selector and serial bus jacquard control system for circular knitting machine[J]. Journal of Textile Research, 2019, 40(1):136-141.
[3] 王罗俊, 彭来湖, 史伟民. 针织选针器小尺度往复摆动特性研究[J]. 轻工机械, 2019(37): 16-22.
WANG Luojun, PENG Laihu, SHI Weimin. Research on dynamic characteristics of reciprocating pendulum stroke of knitting needle selector[J]. Light Industry Machinery, 2019(37):16-22.
[4] 夏天宇, 彭来湖, 史伟民. 选针器压电陶瓷驱动及往复位移特性研究[J]. 针织工业, 2019(11): 15-18.
XIA Tianyu, PENG Laihu, SHI Weimin. Piezoelectric ceramic driving and reciprocating motion characteristics of needle selector[J]. Knitting Industries, 2019(11):15-18.
[5] 项宏年, 袁嫣红, 向忠. 电磁选针器选针刀头摆动驱动力矩的测量[J]. 浙江理工大学学报(自然科学版), 2018, 39(2):189-194.
XIANG Hongnian, YUAN Yanhong, XIANG Zhong. Measurement of the swing driving torque of the needle selection knife head of the electromagnetic needle selector[J]. Journal of Zhejiang Sci-Tech Univer-sity (Natural Sciences Edition), 2018, 39(2):189-194.
[6] 袁嫣红, 徐文炜, 彭来湖. 基于Ansys的磁保持电磁选针器瞬态仿真[J]. 纺织学报, 2018, 39(12):124-130.
YUAN Yanhong, XU Wenwei, PENG Laihu. Transient simulation of magnetic latching needle selector based on Ansys[J]. Journal of Textile Research, 2018, 39(12):124-130.
[7] 李军, 朱方明, 周炯. 应用频闪原理的选针器频率检测系统设计[J]. 纺织学报, 2017, 38(3):138-142.
LI Jun, ZHU Fangming, ZHOU Jiong. Design of needle selector frequency detection system based on stroboscopic principle[J]. Journal of Textile Research, 2017, 38(3):138-142.
[8] 刘仲民, 何胜皎, 胡文瑾, 等. 基于背景减除法的视频序列运动目标检测[J]. 计算机应用, 2017, 37(6):1777-1781.
doi: 10.11772/j.issn.1001-9081.2017.06.1777
LIU Zhongmin, HE Shengjiao, HU Wenjin, et al. Moving object detection based on background subtraction for video sequence[J]. Journal of Computer Applications, 2017, 37(6):1777-1781.
doi: 10.11772/j.issn.1001-9081.2017.06.1777
[9] TAKEUCHI Shunji, NISHIOKA Kazuki, UEMATSU Hideyuki, et al. Research into development of the defect detection system for knitted fabric produced by the circular knitting machines by image analysis[J]. Journal of Textile Engineering, 2018, 64(2):45-49.
doi: 10.4188/jte.64.45
[10] 印平. 2018中国国际纺织机械展览会暨ITMA亚洲展览会圆纬机述评[J]. 针织工业, 2018 (11): 1-8.
YIN Ping. Review of the circular weft knitting machines on china international textile machinery exhibition:2018 ITMA Asia[J]. Knitting Industries, 2018(11):1-8.
[11] 张少强. 基于CMV300的CMOS相机光电响应特性研究[D] 北京: 中国科学院大学, 2015:15-18.
ZHANG Shaoqiang. The study of CMOS camera photoelectric response characteristic based CMV300[D]. Beijing: University of Chinese Academy of Sciences, 2015:15-18.
[12] 台浩杰. 基于线阵相机的不干胶印刷品缺陷检测技术研究[D]. 太原: 中北大学, 2019:13-17.
TAI Haojie. Research on self-adhesive print defect detection technology based on linear array camera[D]. Taiyuan: North University of China, 2019:13-17.
[13] 韩晓勇. 基于图像处理的车辆焊缝间隙检测技术研究[D]. 长春: 长春理工大学, 2019:8-12.
HAN Xiaoyong. Research on vehicle weld gap detection technology based on image processing[D]. Changchun: Changchun University of Science and Technology, 2019:8-12.
[14] ABROSIMOV K N, GERKE K M, SEMENKOV I N, et al. Otsu's algorithm in the segmentation of pore space in soils based on tomographic data[J]. Eurasian Soil Science, 2021, 54(4):560-571.
doi: 10.1134/S1064229321040037
[1] WANG Luojun, PENG Laihu, SHI Weimin, ZHANG Weizhong. Detection technology for electromagnetic needle selection based on piezoelectric adhesive assembly [J]. Journal of Textile Research, 2022, 43(08): 167-175.
[2] DENG Zhongmin, HU Haodong, YU Dongyang, WANG Wen, KE Wei. Density detection method of weft knitted fabrics making use of combined image frequency domain and spatial domain [J]. Journal of Textile Research, 2022, 43(08): 67-73.
[3] MA Yunjiao, WANG Lei, PAN Ruru, GAO Weidong. Calibration method of three-dimensional yarn evenness based on mirrored image [J]. Journal of Textile Research, 2022, 43(07): 55-59.
[4] ZHOU Qihong, PENG Yi, CEN Junhao, ZHOU Shenhua, LI Shujia. Yarn breakage location for yarn joining robot based on machine vision [J]. Journal of Textile Research, 2022, 43(05): 163-169.
[5] ZHANG Ronggen, FENG Pei, LIU Dashuang, ZHANG Junping, YANG Chongchang. Research on on-line detection system of broken filaments in industrial polyester filament [J]. Journal of Textile Research, 2022, 43(04): 153-159.
[6] XIONG Jingjing, YANG Xue, SU Jing, WANG Hongbo. Testing method for fabric moisture conductivity based on image technology [J]. Journal of Textile Research, 2021, 42(12): 70-75.
[7] LÜ Wentao, LIN Qiqi, ZHONG Jiaying, WANG Chengqun, XU Weiqiang. Research progress of image processing technology for fabric defect detection [J]. Journal of Textile Research, 2021, 42(11): 197-206.
[8] XIA Xuwen, MENG Shuo, PAN Ruru, GAO Weidong. On-line detection of warp collision and reed embedding based on improved inter-frame difference method [J]. Journal of Textile Research, 2021, 42(06): 91-96.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
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