Defect inspection of disposable glucose test strips using machine vision

被引:0
|
作者
Yen, Hsu-Nan [1 ]
Lee, Wei Chen [1 ]
机构
[1] St Johns Univ, Dept Elect Engn, New Taipei, Taiwan
关键词
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a machine vision system for defect inspection disposable glucose test strips. The proposed system first lightened and captured image of flawless glucose test strip while offline training. The captured image was processed through image processing technology to establish standard projection data of silver paste and carbon rubber in flawless glucose test strips. Then the measured blood glucose test strips were processed with the same lighting and image processing procedures when online detection. The defects of the measured blood glucose test strips were inspected by the projection data comparison. Experiments show that the proposed system can effectively detect three main defects on glucose test strips, i.e., silver exposing, short circuit of carbon paste electrode and unglued carbon plastic electrode.
引用
收藏
页数:2
相关论文
共 50 条
  • [1] ASPARAGUS DEFECT INSPECTION WITH MACHINE VISION
    RIGNEY, MP
    BRUSEWITZ, GH
    KRANZLER, GA
    [J]. TRANSACTIONS OF THE ASAE, 1992, 35 (06): : 1873 - 1878
  • [2] Bearing defect inspection based on machine vision
    Shen, Hao
    Li, Shuxiao
    Gu, Duoyu
    Chang, Hongxing
    [J]. MEASUREMENT, 2012, 45 (04) : 719 - 733
  • [3] Defect Detection of Scroll Fixed Using AI Machine Vision Inspection
    Lee, Jun-Sik
    Yun, Ki-Cheol
    Park, Jung Kyu
    [J]. International Journal of Precision Engineering and Manufacturing, 2024, 25 (11) : 2311 - 2319
  • [4] Quality prediction through machine learning for the inspection and manufacturing process of blood glucose test strips
    Tsou, Ching-Shih
    Liou, Christine
    Cheng, Longsheng
    Zhou, Hanting
    [J]. COGENT ENGINEERING, 2022, 9 (01):
  • [5] A Machine Vision System for Film Capacitor Defect Inspection
    Liu, Xiu
    Yang, Yuxiang
    Gao, Mingyu
    Huang, Jiye
    He, Zhiwei
    [J]. PROCEEDINGS OF THE 2015 10TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, 2015, : 1413 - 1418
  • [6] Surface defect inspection and classification of segment magnet by using machine vision technique
    Jiang, Honghai
    Yin, Guofu
    [J]. ADVANCED MANUFACTURING SYSTEMS, 2011, 339 : 32 - 35
  • [7] An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods
    Zhou, Qinbang
    Chen, Renwen
    Huang, Bin
    Liu, Chuan
    Yu, Jie
    Yu, Xiaoqing
    [J]. SENSORS, 2019, 19 (03)
  • [8] Defect Inspection of Quartz Crystal Based on Machine Vision
    Chen Zhebo
    Zhang Xin
    Huang Dandan
    [J]. 2013 INTERNATIONAL CONFERENCE ON OPTICAL INSTRUMENTS AND TECHNOLOGY: OPTOELECTRONIC IMAGING AND PROCESSING TECHNOLOGY, 2013, 9045
  • [9] Defect Straw Inspection Method Based on Machine Vision
    Zhu, Ying
    Zhang, Hui
    Zhang, Zhisheng
    Xia, Zhijie
    [J]. 2020 IEEE 3RD INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND SIGNAL PROCESSING (ICICSP 2020), 2020, : 198 - 202
  • [10] A Defect Inspection Method for Machine Vision Using Defect Probability Image with Deep Convolutional Neural Network
    Jang, Chanhee
    Yun, Sangyun
    Hwang, Hyejin
    Shin, Hyunmin
    Kim, SeongSoo
    Park, Yangsub
    [J]. COMPUTER VISION - ACCV 2018, PT I, 2019, 11361 : 142 - 154