基于孪生深度特征融合残差网络的PCB缺陷分类模型

PCB Defect Classification Model Based on Siamese Deep Feature Fusion Residual Network

在电子工业领域中,印刷电路板(printed circuit board,PCB)缺陷检测已经变得越来越重要.PCB的部分微小损伤或者不规则损伤与其密集复杂的排线等视觉纹理信息高度相关.传统卷积神经网络提取特征向量,容易丢失纹理特征等中级视觉特征信息,导致对于细微损伤和不规则损伤的检测效果不明显.针对这一问题,提出了基于孪生深度特征融合残差网络(Siamese deep feature fusion residual network)的PCB损伤分类模型.模型的骨干网络采用ResNet50.特征提取阶段将纹理信息等中级视觉特征和神经网络最终输出的高级语义特征融合为一个32维的特征向量.两个特征的向量的相似性用L2距离表示,用于判断PCB是否有缺陷.在训练阶段应用了三元损失和交叉熵损失,多个损失函数的组合提高了网络的准确性.通过实验验证了模型的有效性,在测试数据集上的准确率达到了(95.42±0.31)%的准确率,实现了模型在PCB缺陷分类检测的可行性.

In the electronic industry,defect detection of printed circuit board(PCB)has become more and more important.Some minor or irregular damage of PCBs is closely related to visual texture information,such as dense and complex PCB cables.Feature vectors extracted from the traditional convolutional neural network are prone to lose the intermediate visual feature information such as texture features,which results in an insignificant detection effect for minor and irregular damage.To solve this problem,this study proposes a PCB damage classification model based on a Siamese deep feature fusion residual network,and the model’s backbone network is ResNet50.In the feature extraction stage,the intermediate visual features such as texture information and the high-level semantic features finally output by the neural network are fused into a 32-dimensional feature vector.The similarity between the vectors of the two features is represented by the L2 distance,which is used to judge whether the PCB is defective.Triplet loss and cross-entropy loss are applied in the training phase,and the combination of multiple loss functions improves the accuracy of the network.The validity of the model is verified by experiments,and the accuracy on the test data set reaches(95.42±0.31)%.This indicates the feasibility of the model in PCB defect classification and detection.