多特征融合的焊缝图像多标签分类算法

Weld Image Multi-Label Classification Algorithm Based on Multi-Feature Fusion

为了实现焊缝缺陷的准确分类,提出一种多特征融合的焊缝图像多标签分类算法。首先,通过残差神经网络(ResNet-50)提取图像的特征信息,根据得到的特征图构建图结构,提出关联度引导邻域传播(RDGNP)算法优化图结构;然后,使用图卷积神经网络(GCN)提取图结构的特征信息,并设计特征融合模块实现图像特征和图结构特征的结合;最后,得到多标签分类结果。实验结果表明:文中算法能够有效地实现焊缝缺陷的多标签分类,在X射线焊缝缺陷数据集上的性能有明显提升。

In order to achieve accurate classification of welding defects,a weld image multi-label classification algorithm based on multi-feature fusion is proposed.Firstly,feature information of images is extracted by a residual neural network(ResNet-50),and the graph structure is constructed based on the obtained feature maps.An algorithm named relation degree guided neighborhood propagation(RDGNP)is proposed to optimize the graph structure.Then,the feature information of the graph structure is extracted using graph convolutional neural network(GCN),and a feature fusion module is designed to achieve the combination of image features and graph structure features.Finally,multi-label classification results are obtained.Experimental results show that the proposed method can effectively realize the multi-label classification of welding defects,and its performance on the X-ray welding defects dataset is significantly improved.