| 增量式块主成分分析的焊缝图像特征提取算法北大核心CSCD |
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| 引用本文： | 张鹏,武刚,任柯光. 增量式块主成分分析的焊缝图像特征提取算法北大核心CSCD[J]. 光电子．激光, 2022, 0(8): 851-857 |
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| 作者姓名： | 张鹏 武刚 任柯光 |
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| 作者单位： | 天津理工大学 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 机电工程国家级实验教学示范中心，天津 300384,天津理工大学 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 机电工程国家级实验教学示范中心，天津 300384,天津理工大学 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 机电工程国家级实验教学示范中心，天津 300384 |
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| 基金项目： | 国家重点研发计划项目(2017YFB1303304)和天津市科技计划重大专项目(17ZXZNGX00110)资助项目 |
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| 摘 要： | 针对焊缝图像特征提取的实时性问题,该文提出一种增量式块主成分分析(incremental block principal component analysis,IBlockPCA)算法,用于焊缝特征主成分的提取。该算法先将焊缝表面图像分割成子图像块并对其进行重构,然后利用提出的IBlockPCA算法对局部块图像进行增量式特征提取,并采用KNN算法对提取的特征主成分进行分类识别;最后在焊缝数据集上进行了算法的性能对比。实验结果表明,该算法在收敛率、分类率及复杂度等方面均优于其他主成分分析(principal component analysis,PCA)算法,其分类识别率为97.5%,其平均处理速度可达50 frame/s,能够满足焊缝表面图像的实时性处理需求。
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| 关 键 词： | 块主成分分析 焊缝图像 特征提取 分类识别 增量迭代 |
| 收稿时间： | 2021-11-28 |
| 修稿时间： | 2021-12-30 |
Feature extraction algorithm of weld image based on incremental block principal component analysis |
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| ZHANG Peng,WU Gang and REN Keguang. Feature extraction algorithm of weld image based on incremental block principal component analysis[J]. Journal of Optoelectronics·laser, 2022, 0(8): 851-857 |
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| Authors: | ZHANG Peng WU Gang REN Keguang |
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| Affiliation: | Tianjin Key Laboratory for Advanced Mechatronical System Design and Intellig ent Control,Tianjin University of Technology,Tianjin 300384, China;National Demonstration Center for Experimental Mechanical and Electrical Engineering Education,Tianjin University of Technology,Tianjin 300384, China,Tianjin Key Laboratory for Advanced Mechatronical System Design and Intellig ent Control,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 Mechatronical System Design and Intellig ent Control,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: | To solve the problems of real-time fe ature extraction on weld surface images,a feature evaluation algorithm based on incremental block principal component analysis (IBlockPCA) is proposed.First,the weld surface images are segmented into sub-image blocks,and then the blocks are reconstructed.Next,the incremental feature extraction is performed on the lo cal block images by using the proposed IBlockPCA,and the KNN is used to classify and recognize the evaluated principal components.Finally,the performances are compared on the weld dataset.The experim ental results show that the IBlockPCA is superior to other principal component analysis (PCA) algorithms in the convergence r ate,classification rate and complexity.The classification rate is 97.5%,and the average processing speed can reach 50 frames per second.It can meet the real-time processing requirements of weld su rface images. |
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| Keywords: | block principal component analysis weld images feature extraction classificat ion and recognition incremental iteration |
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