基于多尺度特征融合的钢材表面缺陷分类方法

Classification Method of Steel Surface Defects Based on Multi-Scale Feature Fusion

针对钢材表面缺陷分类检测率低的问题,文中采用一种基于纹理多尺度特征融合的表面缺陷分类方法。利用Gabor滤波器与灰度共生矩阵建立纹理图像的多尺度特征向量,同时利用卷积运算对纹理图像进行特征提取,并引入混合膨胀卷积模块以增加感受野,将两种特征向量进行融合得到加强后的融合纹理特征向量。融合后的特征以序列方式输入长短时记忆网络(Long Short-Term Memory,LSTM)构建分类模型,利用混淆矩阵将分类结果进行指标评判。结果表明该方法在NEU(Northeastern University)数据集上的分类准确率达到97.5%。文中搭建LSTM网络、BP(Back Propagation)神经网络、SVM(Support Vector Machine)、KNN(K-Nearest Neighbor)以及CART(Classification And Regression Tree)等分类方法进行了对比实验。结果显示,在多尺度下LSTM分类方法表现最好,F1指标最高。结合BP网络、LSTM网络、SVM、KNN、CART、CNN以及AlexNet等方法进行了消融实验,验证了该方法的普适性。该方法充分挖掘了纹理图像的多尺度特征信息,对钢材表面缺陷分类方法的研究具有积极意义。

In view of the low detection rate of steel surface defect classification,a surface defect classification method based on texture multi-scale feature fusion is adopted.Gabor filter and gray level co-occurrence matrix are used to establish multi-scale feature vectors of texture images.At the same time,convolution operation is used to extract features of texture images,and hybrid dilated convolution module is introduced to increase receptive field.The two feature vectors are fused to obtain the enhanced fused texture feature vector.The fused features are input into Long Short-Term Memory(LSTM)network in sequence to build a classification model,and the classification results are evaluated using the confusion matrix.The results show that the classification accuracy of this method on the NEU(Northeastern University)data set is 97.5%.The LSTM network,BP(Back Propagation)neural network,SVM(Support Vector Machine),KNN(K-Nearest Neighbor),CART(Classification And Regression Tree)and other classification methods are set up for comparative experiments.The results show that LSTM classification method performs best in multi-scale,and F1 index is the highest.Ablation experiments are conducted with BP network,LSTM network,SVM,KNN,CART,CNN,AlexNet and other methods to verify the universality of this method.This method fully exploits the multi-scale feature information of the texture image,and has important significance for the research of the classification method of steel surface defects.