Weld defect detection method based on deep subspace learning
Principal Component Analysis Network(PCANet)is a simplified deep subspace learning model based on Convolutional Neural Network(CNN).When PCANet is applied to the weld defect detection,it cannot reflect the complete structure information of data and is sensitive to noise.To solve these problems,a Robust Non-Greedy Bi-Directional two-dimensional PCANet algorithm(RNG-BDPCANet)weld defect online detection method was pro-posed,which used a bi-directional two-dimensional principal component analysis as the convolution kernel under norm distance metric to obtain the optimal global projection matrix of the objective function with a non-greedy strat-egy.It was robust to outliers.The experiments were carried out on the self-built weld artificial dataset,ORL and Yale B face dataset respectively.The results showed that the proposed algorithm had a significant improvement in the classification and robustness performances.
weld defectsprincipal component analysis networkdeep learningtwo-dimensional principal component analysisrobustnessnorm
万方数据
维普
