| 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 |
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| 引用本文： | 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,2023,34(8):872-881. |
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| 作者姓名： | 方建雄 王肖锋 王成林 |
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| 作者单位： | 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,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,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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| 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 |
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| 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。
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| 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 |
| 收稿时间： | 2022/7/23 0:00:00 |
| 修稿时间： | 2022/9/12 0:00:00 |
Research on F-norm-based two-dimensional principal component analysis algorithm for weld surface defect recognition |
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| FANG Jianxiong,WANG Xiaofeng and WANG Chenglin.Research on F-norm-based two-dimensional principal component analysis algorithm for weld surface defect recognition[J].Journal of Optoelectronics·laser,2023,34(8):872-881. |
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| Authors: | FANG Jianxiong WANG Xiaofeng and WANG Chenglin |
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| Affiliation: | Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology,Tianjin 300384, China,Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology,Tianjin 300384, China;National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China and Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology,Tianjin 300384, China;National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China |
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| Abstract: | 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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| Keywords: | two-dimensional principal component analysis (2DPCA) weld surface defect feature extraction defect recognition |
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