A Defect Detection System for Lamp Cup Rivet Based on Machine Vision

被引:0
|
作者
He, Zhiwei [1 ,2 ]
Jiang, Canjun [1 ]
Yang, Yuxiang [1 ,2 ]
Gao, Mingyu [1 ,2 ]
Yu, Zhongfei [1 ]
机构
[1] Hangzhou Dianzi Univ, Sch Elect & Informat, Hangzhou, Zhejiang, Peoples R China
[2] Zhejiang Prov Key Lab Equipment Elect, Hangzhou, Zhejiang, Peoples R China
来源
PROCEEDINGS OF THE 2019 IEEE 16TH INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC 2019) | 2019年
关键词
machine vision; servo controller; rivet detection; least squares;
D O I
10.1109/icnsc.2019.8743260
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In order to improve the production efficiency of traditional industries and reduce production costs. This study designed a set of automatic detection system for lamp cup rivet defects based on the production characteristics of glass lamp cups of machine vision, which greatly improved the detection efficiency. The system uses high-definition industrial cameras, industry personal computer and servo driver to build a hardware platform, using Gaussian Filter, Thresholding method, Contour Extraction, Contour Screening and Least squares to fit the image processing technology, so the inclination degree of the lamp cup rivet and the depth of the groove are detected and analyzed. This study found that the detection of a single lamp cup took about 1s, and the accuracy of it was high. This system has been tested in factory. After a large number of product tests, the system is stable and reliable with high detection efficiency. The system not only meets the requirements of modern production, but also offers a greatly liberates of labor force.
引用
收藏
页码:357 / 362
页数:6
相关论文
共 50 条
  • [21] A Machine Vision System for Defect Detection of a Traveling Grate Conveyor
    Pouramini, Ahmad
    Varaee, Hadi
    2015 2ND INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED ENGINEERING AND INNOVATION (KBEI), 2015, : 1063 - 1066
  • [22] Machine vision system for automatic defect detection of ultrasound probes
    Profili, Andrea
    Magherini, Roberto
    Servi, Michaela
    Spezia, Fabrizio
    Gemmiti, Daniele
    Volpe, Yary
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2024, 135 (7-8): : 3421 - 3435
  • [23] Design of defect detection system for glass fiber plied yarn based on machine vision
    Yang J.
    Jing J.
    Li J.
    Wang Y.
    Fangzhi Xuebao/Journal of Textile Research, 2024, 45 (05): : 193 - 201
  • [24] Machine vision-based cutting process for LCD glass defect detection system
    Chao-Ching Ho
    Hao-Ping Wang
    Yuan-Cheng Chiao
    The International Journal of Advanced Manufacturing Technology, 2022, 123 : 1477 - 1498
  • [25] Machine vision-based cutting process for LCD glass defect detection system
    Ho, Chao-Ching
    Wang, Hao-Ping
    Chiao, Yuan-Cheng
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2022, 123 (5-6): : 1477 - 1498
  • [26] Research on the Application of Steel Plate Surface Defect Detection System Based on Machine Vision
    Liu, Xianfeng
    PROCEEDINGS OF THE 2016 2ND INTERNATIONAL CONFERENCE ON MATERIALS ENGINEERING AND INFORMATION TECHNOLOGY APPLICATIONS (MEITA 2016), 2017, 107 : 244 - 248
  • [27] PEACH DEFECT DETECTION WITH MACHINE VISION
    MILLER, BK
    DELWICHE, MJ
    TRANSACTIONS OF THE ASAE, 1991, 34 (06): : 2588 - 2597
  • [28] Medicine Glass Bottle Defect Detection Based on Machine Vision
    Fu, Li
    Zhang, Shuai
    Gong, Yu
    Huang, Quanjun
    PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019), 2019, : 5681 - 5685
  • [29] Surface Defect Detection of Plaster Coating Based on Machine Vision
    Wu, Huan
    Luo, Huifu
    Zhu, Wei
    YanghongWang
    Zhang, Qiang
    Ma, Binwu
    Yang, Yanzhu
    Fan, Hui
    Xu, Hongwei
    PROCEEDINGS OF 2017 IEEE INTERNATIONAL CONFERENCE ON UNMANNED SYSTEMS (ICUS), 2017, : 277 - 281
  • [30] Defect detection for CCGA solder column based on machine vision
    Hou, Qili
    Wang, Yang
    Chen, Banghua
    Xu, Song
    Yang, Lihong
    2024 16TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING, ICMLC 2024, 2024, : 264 - 270