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方建雄,王肖锋,王成林.基于F范数的二维主成分分析算法及焊缝表面缺陷识别研究[J].光电子激光,2023,(8):872~881

基于F范数的二维主成分分析算法及焊缝表面缺陷识别研究

Research on F-norm-based two-dimensional principal component analysis algorithm for weld surface defect recognition

投稿时间：2022-07-23 修订日期：2022-09-12

DOI：

中文关键词: 二维主成分分析(2DPCA) 焊缝表面缺陷特征提取缺陷识别

英文关键词:two-dimensional principal component analysis (2DPCA) weld surface defect feature extraction defect recognition

基金项目:国家重点研发计划 (2018AAA0103004)和天津市科技计划重大专项 (20YFZCGX00550)资助项目

作者	单位
方建雄	天津理工大学机械工程学院，天津市先进机电系统设计与智能控制重点实验室,天津 300384
王肖锋	天津理工大学机械工程学院，天津市先进机电系统设计与智能控制重点实验室,天津 300384 机电工程国家级实验教学示范中心，天津理工大学，天津 300384
王成林	天津理工大学机械工程学院，天津市先进机电系统设计与智能控制重点实验室,天津 300384 机电工程国家级实验教学示范中心，天津理工大学，天津 300384

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中文摘要:

针对传统二维主成分分析(two-dimensional principal component analysis,2DPCA)算法应用于焊缝表面缺陷识别中存在重构性能及鲁棒性较弱等问题,本文将最大化投影距离和最小化重构误差引入到目标函数中,提出了一种基于F范数的非贪婪二维主成分分析算法(non-greedy 2DPCA with F-norm,NG-2DPCA-F),该算法具有良好的鲁棒性和较低的重构误差。为了进一步提取图像的结构信息和求解出维数更小的特征矩阵,进而提出一种基于F范数的非贪婪双向二维主成分分析算法(non-greedy bilateral 2DPCA with F-norm,NG-B2DPCA-F)。最后,以含有不同噪声块的焊缝表面图像数据集进行实验,结果表明,本文所提算法在平均重构误差、重构图像与分类识别实验中均表现出良好的鲁棒性能。

英文摘要:

Aiming at the problems of weak reconstruction performance and robustness in the traditional two-dimensional principal component analysis (2DPCA) algorithm applied to weld surface defect detection,maximizing the projection distance and minimizing the reconstruction error are introduced into the objective function as optimization objectives.And a non-greedy two-dimensional principal component analysis algorithm based on F-norm (non-greedy 2DPCA with F-norm, NG-2DPCA-F) is proposed.This algorithm has good robustness and low reconstruction error.In order to further extract the structural information of the image and obtain the feature matrix with smaller dimension,this paper proposes a bidirectional two-dimensional principal component analysis algorithm based on F-norm (non-greedy bilateral 2DPCA with F-norm,NG-B2DPCA-F).The experiments are carried out with weld surface images with different noise blocks as datasets.The results demonstrate that the proposed algorithm has good robustness in the average reconstruction error,reconstruction image and classification experiments.

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