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基于激光视觉的钢结构焊缝图像处理系统

王树强, 周游, 陈昊雷, 陈钊, 韩彦林

王树强, 周游, 陈昊雷, 陈钊, 韩彦林. 基于激光视觉的钢结构焊缝图像处理系统[J]. 焊接学报, 2022, 43(2): 101-105, 112. DOI: 10.12073/j.hjxb.20210603001
引用本文: 王树强, 周游, 陈昊雷, 陈钊, 韩彦林. 基于激光视觉的钢结构焊缝图像处理系统[J]. 焊接学报, 2022, 43(2): 101-105, 112. DOI: 10.12073/j.hjxb.20210603001
WANG Shuqiang, ZHOU You, CHEN Haolei, CHEN Zhao, HAN Yanlin. Image processing system of welding seam of steel structure based on laser vision[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(2): 101-105, 112. DOI: 10.12073/j.hjxb.20210603001
Citation: WANG Shuqiang, ZHOU You, CHEN Haolei, CHEN Zhao, HAN Yanlin. Image processing system of welding seam of steel structure based on laser vision[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(2): 101-105, 112. DOI: 10.12073/j.hjxb.20210603001

基于激光视觉的钢结构焊缝图像处理系统

基金项目: 辽宁省科学技术基金(20170540722)
详细信息
    作者简介:

    王树强,博士,副教授;主要研究方向为智能制造及设备智能化控制;Email: 13898147881@126.com

  • 中图分类号: TG409

Image processing system of welding seam of steel structure based on laser vision

  • 摘要: 针对钢结构件品种多、批量小,焊缝形状位置一致性差,机器人重复定位过程复杂等缺点,设计了一种基于激光视觉的钢结构焊缝图像处理系统. 运用CCD工业相机和激光器,采集带有激光条带的焊缝图像,分别利用中值滤波柔化噪音,Otsu算法自适应阈值分割,开操作和形态学处理相结合去除图像中除目标像素外的小连通区域,提取激光条带的中心线,最终利用Hough变换对中心线直线拟合,得到特征点位置,并通过骨支架试验验证该技术的可行性. 结果表明,该方法可快速准确地检测到焊缝特征点,满足实际要求.
    Abstract: Aiming at the shortcomings of many types of steel structure parts, small batches, poor consistency of weld shape and position, and complicated repetitive positioning process of robots, a laser vision-based steel structure weld image processing system is designed. The technology uses CCD industrial cameras and lasers, Collect welding seam images with laser stripes, use median filtering to soften noise, otsu algorithm adaptive threshold segmentation, open operation and morphological processing to remove small connected areas in the image except target pixels, extract laser stripes Finally, the Hough transform is used to fit the center line of the center line to obtain the position of the feature point, and the feasibility of the technology is verified by the bone scaffold test. The test shows that this method can detect the weld feature points quickly and accurately actual requirements, and meet the actual requirements.
  • 图  1   图像采集示意图

    Figure  1.   Schematic diagram of image acquisition

    图  2   焊接机器人现场工作图

    Figure  2.   On-site work drawing of welding robot

    图  3   图像处理流程

    Figure  3.   Image processing flow

    图  4   中值滤波处理图像

    Figure  4.   Median filter processing image. (a) original weld image; (b) median filtered image

    图  5   二值化图像开操作处理

    Figure  5.   Binary image opening operation processing. (a) binary image; (b) open operations manipulate images

    图  6   开操作处理后骨架提取图像

    Figure  6.   Skeleton extraction image after opening operation processing. (a) morphological removal of image inliers; (b) skeleton extraction; (c) skeleton thinning

    图  7   图像空间与参数空间的映射关系

    Figure  7.   The mapping relationship between image space and parameter space. (a) image space; (b) parameter space

    图  8   Hough变换直线拟合图像

    Figure  8.   Hough transform straight line fitting image

    图  9   焊接机器人和焊接结果

    Figure  9.   Welding robot and welding result. (a) welding robot; (b) welding result

    图  10   中心测量点偏差对比

    Figure  10.   Deviation comparison diagram of center measuring point

    表  1   焊接装置硬件明细表

    Table  1   Hardware list of welding device

    机器人相机图像采集卡激光器
    杭州松欧自动化
    设备有限公司
    上海方诚光电
    科技有限公司
    NI
    公司
    长春镭仕光电
    科技有限公司
    FD-V6 IK200M-12 PCIe-1427 MW-RL-635
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-02
  • 录用日期:  2022-02-14
  • 网络出版日期:  2022-02-18
  • 刊出日期:  2022-04-12

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