Scratch detection of round buttons based on machine vision

被引:0
|
作者
Kong, Lingfeng [1 ]
Wu, Qingxiang [1 ]
Lin, Kai [2 ]
Chen, Baolin [2 ]
机构
[1] Fujian Normal Univ, Coll Photon & Elect Engn, Minist Educ, Key Lab OptoElect Sci & Technol Med, Fuzhou 350007, Fujian, Peoples R China
[2] Fujian Normal Univ, Coll Photon & Elect Engn, Fujian Prov Key Lab Photon Technol, Fuzhou 350007, Fujian, Peoples R China
来源
2017 10TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI) | 2017年
关键词
round button; scratchs; detect; Inhibition;
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
One of the main quality problems of buttons is scratches. The use of machine vision to detect round button scratches can improve work efficiency and reduce costs. This paper has proposed a set of algorithms to improve the scratch detection. As the button has a plurality of ring regions, and the color of each ring region is not the same, Hough transform can be used to extract the center and radius of circular button, then polar transformation is used to convert the circle into a rectangle. It can be faster to divide the different annular regions and easier to partition a button image and calculate binary thresholds of partitions. Button surface is unsmooth and uneven dyeing, so it will interfere the detection of scratches. This paper presents an algorithm for suppression of ring texture, it can effectively reduce the button background irregular texture interference. As the same ring area may exist a variety of colors and the textures of different ring areas are not similar each other, in this paper an algorithm for finding binary thresholds for annular partitions is proposed to reduce interference of scratch judgment. In fact, many scratches are not continuous but intermittent. An intermittent scratch detection algorithm is also proposed to detect intermittent scratches. Combining all the algorithms the scratches in complex round buttons can be well detected.
引用
收藏
页数:6
相关论文
共 50 条
  • [21] Edge Detection of Screw Thread Based on Machine Vision
    Dai, Guocheng
    Wei, Hengzheng
    Luo, Zai
    Jiang, Wensong
    INTERNATIONAL CONFERENCE ON OPTICAL AND PHOTONIC ENGINEERING, ICOPEN 2022, 2022, 12550
  • [22] Cylindrical Label Defect Detection Based on Machine Vision
    Zhao, Yong Xin
    Zhou, Qing Hua
    Proceedings of SPIE - The International Society for Optical Engineering, 2023, 12916
  • [23] Sows parturition detection method based on machine vision
    Liu, Longshen
    Shen, Mingxia
    Bo, Guangyu
    Zhou, Bo
    Lu, Mingzhou
    Yang, Xiaojing
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2014, 45 (03): : 237 - 242
  • [24] Machine Vision Based Fire Detection Techniques: A Survey
    S. Geetha
    C. S. Abhishek
    C. S. Akshayanat
    Fire Technology, 2021, 57 : 591 - 623
  • [25] Detection of Nano-particles Based on Machine Vision
    Wei, Yadong
    Chen, Han
    Wang, Hongcheng
    Wei, Dongshan
    Wu, Yunxia
    Fan, Kaifu
    2019 IEEE INTERNATIONAL CONFERENCE ON MANIPULATION, MANUFACTURING AND MEASUREMENT ON THE NANOSCALE (IEEE 3M-NANO), 2019, : 189 - 192
  • [26] Driver Fatigue Detection System Based on Machine Vision
    Zhang, Zhibin
    Chen, Yangzhou
    Yang, Yuzhen
    2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, : 3979 - 3984
  • [27] Automated Detection of Sick Pigs Based On Machine Vision
    Zhu, Weixing
    Pu, Xuefeng
    Li, Xincheng
    Zhu, Xiaofang
    2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 2, 2009, : 790 - 794
  • [28] Tear detection of conveyor belt based on machine vision
    Wang, Honglei
    Li, Jiacheng
    Wu, Taihui
    Liu, Xiaoming
    Zhang, Junsheng
    INTERNATIONAL CONFERENCE ON INTELLIGENT EQUIPMENT AND SPECIAL ROBOTS (ICIESR 2021), 2021, 12127
  • [29] Tipping paper detection device based on machine vision
    Wang, Hui
    Cheng, Xiaohu
    Zhao, Shuhua
    Zhao, Jufeng
    Tobacco Science and Technology, 2015, 48 (08): : 88 - 92
  • [30] Machine vision based online detection of PCB defect
    Liu, Zhichao
    Qu, Baida
    MICROPROCESSORS AND MICROSYSTEMS, 2021, 82