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基于改正二维主成分分析的焊缝表面缺陷检测

周兆逸, 张亚南, 王肖锋, 刘军

周兆逸, 张亚南, 王肖锋, 刘军. 基于改正二维主成分分析的焊缝表面缺陷检测[J]. 焊接学报, 2021, 42(11): 70-76. DOI: 10.12073/j.hjxb.20210412001
引用本文: 周兆逸, 张亚南, 王肖锋, 刘军. 基于改正二维主成分分析的焊缝表面缺陷检测[J]. 焊接学报, 2021, 42(11): 70-76. DOI: 10.12073/j.hjxb.20210412001
ZHOU Zhaoyi, ZHANG Yanan, WANG Xiaofeng, LIU Jun. Weld surface defect detection based on improved two-dimensional principal component analysis[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2021, 42(11): 70-76. DOI: 10.12073/j.hjxb.20210412001
Citation: ZHOU Zhaoyi, ZHANG Yanan, WANG Xiaofeng, LIU Jun. Weld surface defect detection based on improved two-dimensional principal component analysis[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2021, 42(11): 70-76. DOI: 10.12073/j.hjxb.20210412001

基于改正二维主成分分析的焊缝表面缺陷检测

基金项目: 国家重点研发计划项目 (2017YFB1303304);国家自然科学基金青年项目(52005370);天津市科技计划智能制造重大专项(17ZXZNGX00110) ;天津市大学生创新创业训练计划项目(202010060033).
详细信息
    作者简介:

    周兆逸,硕士;主要从事无损检测和机器学习方面的工作; Email: China_zhouzhaoyi@163.com

    通讯作者:

    王肖锋,副教授;Email: wangxiaofeng@tjut.edu.cn.

  • 中图分类号: TG 441.7

Weld surface defect detection based on improved two-dimensional principal component analysis

  • 摘要: 机器人焊接因零件形状不规则和焊接工艺复杂不可避免带来各种焊缝缺陷. 针对二维主成分分析应用于焊缝表面缺陷检测时面临计算复杂度高、分类准确率低及无法进行增量学习等问题,提出了一种基于均值更新的增量二维主成分分析(mean updated incremental two-dimensional principal component analysis,MUI2DPCA)算法,并将MUI2DPCA和前馈神经网络( feedforward neural network,FNN)相结合进行焊缝表面缺陷在线检测. 首先,对相机捕获的视频帧图像进行预处理得到焊缝局部块图像. 然后,利用MUI2DPCA在线提取局部块图像的模式特征. MUI2DPCA对图像的特征主成分进行增量迭代估计,降低计算复杂度,并且能够增量更新当前的样本均值,减少无关特征变化对主成分收敛性的影响. 最后,利用FNN建立提取的模式特征与焊缝类别之间的联系,实时返回焊缝表面缺陷的检测信息. 试验结果表明,该检测方法平均分类准确率为95.40%,平均处理速度可达29帧/s,能够满足焊缝在线检测的实时性要求.
    Abstract: Robot welding inevitably brings various weld defects due to the irregularity of part shape and the complexity of welding process. When applying two-dimensional principal component analysis to the detection of weld surface defects, it often faces problems such as high computational complexity, low classification accuracy and inability to incrementally learn. To resolve these problems, a mean updated incremental two-dimensional principal component analysis (MUI2DPCA) algorithm is proposed. MUI2DPCA and feedforward neural network (FNN) are combined to detect weld surface defects online. Firstly, the local block images are obtained by preprocessing the video frame images captured by the camera. Then, the pattern features of weld block images are extracted online by MUI2DPCA. The algorithm performs incremental iterative estimation on principal components of block images, significantly reduces the computational complexity, and incrementally updates the current sample mean to reduce the influence of irrelevant feature changes on the convergences of principal components. Finally, FNN is used to establish the relationship between pattern features and weld categories, and the weld defect detection information is obtained in real time. The test results show that the average classification accuracy of the detection method is 95.40%, and the average processing speed reaches 29 frames per second, and it can meet the real-time requirements of weld detection.
  • 图  1   焊接试验平台

    Figure  1.   Welding experimental platform

    图  2   焊缝区域图像预处理

    Figure  2.   Image preprocessing of weld regions. (a) original image of weld; (b) median and mean filter; (c) sobel operator and Otsu algorithm; (d) morphological dilation processing; (e) connected component; (f) weld regions after preprocessing

    图  3   局部块图像分割

    Figure  3.   Local block segmentation

    图  4   3层前馈神经网络

    Figure  4.   Three-layer feedforward neural network

    图  5   焊缝数据集图像样本

    Figure  5.   Image samples of weld database. (a) qualified weld images; (b) welding spatter images; (c) welding tumor images; (d) gas cavity images

    图  6   前8个特征值收敛率

    Figure  6.   Convergence rates of the first eight eigenvalues. (a) first four eigenvalues; (b) last four eigenvalues

    图  7   焊接试验平台检测结果

    Figure  7.   Test results of welding experiment platform

    表  1   $ k $阶特征向量评价值

    Table  1   Evaluation value of the first $k$-order eigenvectors

    特征向量阶数k(个)重构误差E运行时间t/s评价值Q
    24 185.9918.70955.09
    43 762.5631.77354.19
    63 519.4447.37756.01
    83 344.4063.38259.55
    103 219.0080.71264.87
    123 086.9096.11870.23
    142 972.33112.48476.66
    下载: 导出CSV

    表  2   MUI2DPCA和2DPCA分类准确率及复杂度试验

    Table  2   Classification accuracy and complexity experiments of MUI2DPCA and 2DPCA

    算法样本数
    n(个)
    分类准确率
    $ \eta $(%)
    运行时间
    t/s
    内存
    M/kB
    MUI2DPCA20094.03.9926 500
    40094.58.1746 504
    60095.011.7946 483
    80095.015.5536 516
    1 00096.519.0946 508
    2DPCA20094.02.91718 202
    40094.09.23233 575
    60094.018.95648 950
    80094.531.56064 257
    1 00094.547.46579 610
    下载: 导出CSV

    表  3   焊接试验平台检测焊缝缺陷所需的时间

    Table  3   Required time of weld defect detection using the platform running test

    检测步骤时间t/s
    图像预处理32.694
    特征提取2.847
    FNN 分类0.454
    跨帧优化0.978
    总计36.973
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-04-11
  • 网络出版日期:  2021-12-30
  • 刊出日期:  2021-11-24

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