Journal of South China University of Technology(Natural Science Edition)

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Weld Seam Feature Point Recognition Analysis Based on Improved Mean-shift Algorithm

GAO Xiangdong LI Yangjin LIU Xiuhang ZHANG Yanxi YOU Deyong   

  1. Guangdong Provincial Welding Engineering Technology Research Center，Guangdong University of Technology， Guangzhou 510006，Guangdong，China
  • Received:2019-01-02 Online:2019-04-25 Published:2019-03-01
  • Contact: 高向东(1963-)，男，教授，博士生导师，主要从事焊接自动化、焊接质量控制等研究． E-mail:gaoxd@ gdut.edu.cn
  • About author:高向东(1963-)，男，教授，博士生导师，主要从事焊接自动化、焊接质量控制等研究．
  • Supported by:
    Supported by the National Natural Science Foundation of China(51675104)，Guangdong Provincial Department of Education Innovation Team Project(2017KCXTD010)and the Science and Technology Planning Project of Guangdong Province， China(2016A010102015)

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Abstract: A fast and accurate weld seam feature point recognition is the key of the weld seam tracking system based on structured light sensing． For the streak discontinuity caused by the deformation of structured light stripe line at the weld，tracking tests were carried on butt and lap welds of stainnless steel board，and an improved mean-shift algorithm was proposed to extract the feature point of a weld seam． Unlike the traditional algorithm，the improved algorithm eliminated the process of extracting fringe center line and fitting fringe lineand the feature points of welding seam are identified by drift． In order to prevent from back-shifting，the search direction of the algorithm was limited． To improve the running efficiency，a shifting accelerating factor was introduced． The test shows that the improved mean-shift algorithm can effectively recognize the feature point of a weld seam and saliently improve the accuracy and real-time performance．

Key words: structured light, weld feature point, mean-shift, feature point extraction

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