Volume 17 Issue 2
Mar.  2024
Turn off MathJax
Article Contents
JIANG Shi-fei, ZHANG Zhao-guo, WANG Fa-an, XIE Kai-ting, WANG Cheng-lin, LI Zhi. Design of optical field of vision imaging for defect detection of paper and transparent film[J]. Chinese Optics, 2024, 17(2): 354-365. doi: 10.37188/CO.2023-0134
Citation: JIANG Shi-fei, ZHANG Zhao-guo, WANG Fa-an, XIE Kai-ting, WANG Cheng-lin, LI Zhi. Design of optical field of vision imaging for defect detection of paper and transparent film[J]. Chinese Optics, 2024, 17(2): 354-365. doi: 10.37188/CO.2023-0134

Design of optical field of vision imaging for defect detection of paper and transparent film

Funds:  Supported by National Key R & D Program of China (No. 2022YFD2002004); Yunnan Province Academician (Expert) Workstation Project (No. 202105AF150030); Key Project of China Tobacco Industry in Yunnan (No. 2022ZK05)
More Information
  • Corresponding author: wfa@kust.edu.cn
  • Received Date: 09 Aug 2023
  • Rev Recd Date: 12 Sep 2023
  • Available Online: 05 Dec 2023
  • To achieve synchronous detection of defects in the paper and transparent film layers of packaging boxes, we studied the synchronous imaging of the paper and film defect. Firstly, we established models for a standard sphere integral light field, an ellipsoidal integral light field, and an arc integral light field. We then simulated three different light fields using COMSOL Multiphysics 5.6 and compared their ray angle uniformity and irradiation uniformity. The parameters of ellipsoidal area integral light field are optimized by orthogonal simulation. Secondly, the packaging box was imaged using the ellipsoidal integral light field, the bright and dark field forward lighting. Physical detection and machine vision were used to detect five common defects in the packaging box, including oil stains, pressure marks, openings, bubble wrinkles, and breakages, to verify the effectiveness of defect imaging. The results show that the images can clearly present defect characteristics in the paper base and transparent film layers under ellipsoidal integral light field. The physical detection rates for oil stains, pressure marks, openings, bubble wrinkles, and breakages were 96.2%, 92.5%, 100%, 95%, and 92%, respectively. Anomaly detection rates were 98.6%, 97.5%, 100%, 100%, 98.4%, respectively. Detection rates of defects were 97.6%, 96%, 100%, 97%, and 96%, respectively. This study indicates that the consistent optical path angle and irradiation intensity result in a uniform ellipsoidal integral light field. Consequently, transparent film imaging of the packaging box shows clear defect characteristics that satisfy the standards for industrial detection application.

     

  • loading
  • [1]
    姜涛, 张桂林, 高俊鹏. 面向机器视觉检测的缸体横孔照明[J]. 中国光学,2020,13(6):1285-1292. doi: 10.37188/CO.2020-0054

    JIANG T, ZHANG G L, GAO J P. Illumination of a cylinder block transverse hole for machine vision inspection[J]. Chinese Optics, 2020, 13(6): 1285-1292. (in Chinese). doi: 10.37188/CO.2020-0054
    [2]
    梁霄, 李家炜, 赵小龙, 等. 基于深度学习的红外目标成像液位检测方法[J]. 光学学报,2021,41(21):2110001. doi: 10.3788/AOS202141.2110001

    LIANG X, LI J W, ZHAO X L, et al. Infrared target imaging liquid level detection method based on deep learning[J]. Acta Optica Sinica, 2021, 41(21): 2110001. (in Chinese). doi: 10.3788/AOS202141.2110001
    [3]
    赵鹏鹏, 李庶中, 李迅, 等. 融合视觉显著性和局部熵的红外弱小目标检测[J]. 中国光学,2022,15(2):267-275. doi: 10.37188/CO.2021-0170

    ZHAO P P, LI SH ZH, LI X, et al. Infrared dim small target detection based on visual saliency and local entropy[J]. Chinese Optics, 2022, 15(2): 267-275. (in Chinese). doi: 10.37188/CO.2021-0170
    [4]
    王荣昌, 王峰, 任帅军, 等. 基于双流融合网络的单兵伪装偏振成像检测[J]. 光学学报,2022,42(9):0915001.

    WANG R CH, WANG F, REN SH J, et al. Polarization imaging detection of individual camouflage based on two-stream fusion network[J]. Acta Optica Sinica, 2022, 42(9): 0915001. (in Chinese).
    [5]
    金鹏, 黄浩, 刘检华, 等. 多传感器信息融合的铁路扣件缺陷检测方法[J]. 机械工程学报,2021,57(20):38-46. doi: 10.3901/JME.2021.20.038

    JIN P, HUANG H, LIU J H, et al. Fault detection method of railway fastener combined with multi-sensor information[J]. Journal of Mechanical Engineering, 2021, 57(20): 38-46. (in Chinese). doi: 10.3901/JME.2021.20.038
    [6]
    余佳杰, 周建平, 薛瑞雷, 等. 基于结构光视觉和光照模型的焊缝表面质量检测[J]. 中国激光,2022,49(16):1602019.

    YU J J, ZHOU J P, XUE R L, et al. Weld surface quality detection based on structured light and illumination model[J]. Chinese Journal of Lasers, 2022, 49(16): 1602019. (in Chinese).
    [7]
    田萱, 王子亚, 王建新. 基于语义分割的食品标签文本检测[J]. 农业机械学报,2020,51(8):336-343. doi: 10.6041/j.issn.1000-1298.2020.08.037

    TIAN X, WANG Z Y, WANG J X. Text detection of food labels based on semantic segmentation[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(8): 336-343. (in Chinese). doi: 10.6041/j.issn.1000-1298.2020.08.037
    [8]
    郜明. 基于机器视觉的抽纸软包装缺陷检测算法研究[D]. 成都: 四川大学, 2021.

    GAO M. Research on defect detection algorithm of flexible packaging tissue paper on machine vision[D]. Chengdu: Sichuan University, 2021. (in Chinese).
    [9]
    赵宝水. 基于机器视觉的药品包装检测关键技术的研究[D]. 锦州: 辽宁工业大学, 2021.

    ZHAO B SH. Research on the key technology of drug packaging detection based on machine vision[D]. Jinzhou: Liaoning University of Technology, 2021. (in Chinese).
    [10]
    陈雪纯, 方宇伦, 杜世昌, 等. 基于深度学习的包装缺陷快速检测方法[J]. 机械设计与研究,2021,37(6):165-169,178. doi: 10.13952/j.cnki.jofmdr.2021.0237

    CHEN X CH, FANG Y L, DU SH CH, et al. Rapid packaging defect detection method based on deep learning[J]. Machine Design and Research, 2021, 37(6): 165-169,178. (in Chinese). doi: 10.13952/j.cnki.jofmdr.2021.0237
    [11]
    陈其浩, 孙林, 张倩. 基于改进U2-Net的透明件划痕检测方法[J]. 科学技术与工程,2022,22(2):620-627. doi: 10.3969/j.issn.1671-1815.2022.02.025

    CHEN Q H, SUN L, ZHANG Q. Scratch detection method of transparent parts based on improved U2-Net[J]. Science Technology and Engineering, 2022, 22(2): 620-627. (in Chinese). doi: 10.3969/j.issn.1671-1815.2022.02.025
    [12]
    JIN ZH X, ZHONG F Y, ZHANG Q, et al. Visual detection of tobacco packaging film based on apparent features[J]. International Journal of Advanced Robotic Systems, 2021, 18(3): 1-14.
    [13]
    XU Y CH, NAGAHARA H, SHIMADA A, et al. TransCut2: transparent object segmentation from a light-field image[J]. IEEE Transactions on Computational Imaging, 2019, 5(3): 465-477. doi: 10.1109/TCI.2019.2893820
    [14]
    MADESSA A H, DONG J Y, DONG X H, et al. Leveraging an instance segmentation method for detection of transparent materials[C]. 2019 IEEE Conference on SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, IEEE, 2019: 406-412.
    [15]
    WANG J J, LI R F, MO X T, et al. Web film inspection system[J]. Optik, 2009, 120(13): 630-635. doi: 10.1016/j.ijleo.2008.01.001
    [16]
    REN ZH H, FANG F ZH, YAN N, et al. State of the art in defect detection based on machine vision[J]. International Journal of Precision Engineering and Manufacturing-Green Technology, 2022, 9(2): 661-691. doi: 10.1007/s40684-021-00343-6
    [17]
    LEE D J, SCHOENBERGER R, ARCHIBALD J, et al. Development of a machine vision system for automatic date grading using digital reflective near-infrared imaging[J]. Journal of Food Engineering, 2008, 86(3): 388-398. doi: 10.1016/j.jfoodeng.2007.10.021
    [18]
    AL-MALLAHI A, KATAOKA T, OKAMOTO H, et al. Detection of potato tubers using an ultraviolet imaging-based machine vision system[J]. Biosystems Engineering, 2010, 105(2): 257-265. doi: 10.1016/j.biosystemseng.2009.11.004
    [19]
    MERY D, LILLO I, LOEBEL H, et al. Automated fish bone detection using X-ray imaging[J]. Journal of Food Engineering, 2011, 105(3): 485-492. doi: 10.1016/j.jfoodeng.2011.03.007
    [20]
    LI Y L, WANG SH J, TIAN Q, et al. A survey of recent advances in visual feature detection[J]. Neurocomputing, 2015, 149: 736-751. doi: 10.1016/j.neucom.2014.08.003
    [21]
    MORENO I, AVENDAÑO-ALEJO M, TZONCHEV R I. Designing light-emitting diode arrays for uniform near-field irradiance[J]. Applied Optics, 2006, 45(10): 2265-2272. doi: 10.1364/AO.45.002265
    [22]
    LIU Y J, KONG J Y, WANG X D, et al. Research on image acquisition of automatic surface vision inspection systems for steel sheet[C]. 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), IEEE, 2009: 189-192.
    [23]
    赵文宏, 彭超, 陈红星. 基于形态学玻璃屏幕表面划痕检测方法研究[J]. 浙江工业大学学报,2016,44(3):279-282. doi: 10.3969/j.issn.1006-4303.2016.03.009

    ZHAO W H, PENG CH, CHEN H X. Study on scratch inspection methods of glass screen surface based on morphology[J]. Journal of Zhejiang University of Technology, 2016, 44(3): 279-282. (in Chinese). doi: 10.3969/j.issn.1006-4303.2016.03.009
    [24]
    刘重阳. 基于计算机视觉的透明材料缺陷检测系统研究[D]. 哈尔滨: 哈尔滨理工大学, 2020.

    LIU CH Y. Research on defect detection system of transparent materials based on computer vision[D]. Harbin: Harbin University of Science and Technology, 2020. (in Chinese).
    [25]
    苑玮琦, 毕天宇. 玻璃质量在线视觉检测系统光源的设计[J]. 应用光学,2015,36(3):369-375. doi: 10.5768/JAO201536.0301006

    YUAN W Q, BI T Y. Illuminating source design of online visual inspection system for glass defects[J]. Journal of Applied Optics, 2015, 36(3): 369-375. (in Chinese). doi: 10.5768/JAO201536.0301006
    [26]
    MEZIANE R, MEGUELLATI S, MESSAGIER M. Precision inspection of transparent component quality[J]. The International Journal of Advanced Manufacturing Technology, 2023, 125(3-4): 1731-1741. doi: 10.1007/s00170-022-10774-3
    [27]
    张以谟. 应用光学[M]. 4版. 北京: 电子工业出版社, 2015.

    ZHANG Y M. Applied Optics[M]. 4th ed. Beijing: Publishing House of Electronics Industry, 2015. (in Chinese)
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(20)  / Tables(6)

    Article views(299) PDF downloads(120) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return