上海航天（中英文）2025，Vol.42Issue(1)：149-156,8.DOI:10.19328/j.cnki.2096-8655.2025.01.016
基于鲁棒主成分分析的运载火箭焊缝射线数字成像分类方法
A Classification Method for Rocket Weld Seam Radiographic Digital Imaging Based on Robust Principal Component Analysis
摘要
Abstract
The automatic detection technology for digital radiographic images of launch vehicle welds primarily involves the classification of digital radiographic images of launch vehicle welds.However,the actual production process yields an enormous volume of images,and annotating the entire dataset would entail a considerable waste of manpower and resources.Considering that prior supervisory information can enhance the precision of target extraction and that removing the background from images can improve classification accuracy,this paper proposes a Semi-supervised Target Feature Extraction algorithm with Laplacian Eigenmaps(LE)Regularization based on Robust Principal Component Analysis(RPCA),termed SSRLE.On the premise of ensuring the global structure of the data,the local structure of the data is guaranteed by adding the LE regularization of the weight matrix of the adaptive neighborhood graphs,and the influence of the nearest neighbor value k in the classical LE algorithm is excluded.Under the influence of prior information,the target and background are separated effectively.The linear classifier is trained with the target data and supervision information.With the manifold smoothing hypothesis,the trained linear classifier can predict unlabeled data,resulting in improved classification results.Finally,experiments are carried out,and the classification effects of different semi-supervised algorithms are compared.The results show that the proposed method is valid,and is superior to other methods.关键词
运载火箭贮箱/鲁棒主成分分析/视觉目标特征提取/流形学习/半监督学习Key words
rocket fuel tank/robust principal component analysis/visual target feature extraction/manifold learning/semi-supervised learning分类
计算机与自动化引用本文复制引用
王宁,刘晓,刘骁佳,危荃..基于鲁棒主成分分析的运载火箭焊缝射线数字成像分类方法[J].上海航天（中英文）,2025,42(1):149-156,8.基金项目
国家自然科学基金资助项目(92267201) （92267201）
