基于深度子空间学习的焊缝缺陷检测方法

Weld defect detection method based on deep subspace learning

主成分分析网络(PCANet)是一个基于简化的卷积神经网络的深度子空间学习模型。针对PCANet算法应用于焊缝缺陷检测时无法体现数据完整结构信息、对噪声较敏感等问题,在PCANet的基础上提出一种鲁棒非贪婪双向二维PCANet(RNG-BDPCANet)焊缝缺陷在线检测方法。RNG-BDPCANet在范数距离度量标准下,利用双向二维主成分分析作卷积核,并采用非贪婪策略得到目标函数最优的整体投影矩阵,对离群值具有较强的鲁棒性。最后,在自建的焊缝人工数据集、ORL和Yale B人脸数据集上分别进行实验。结果表明,所提出的算法在分类性能方面得到显著提高,具有较强的鲁棒性能。

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 proposed,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 strategy.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.