卷积神经网络金相组织自动识别

Automatic identification of metallographic structure based on convolutional neural network

为了降低人工分辨金相组织图像类别的误差率,提高分辨效率,采用卷积神经网络模型对金相组织图像进行自动辨识。对制备金相样块所得铁素体与马氏体两种金相组织图像进行分析,提出符合金相组织图像分布特征的预处理方案。通过采用图像尺寸归一化、灰度值归一化以及高斯平滑处理等方法,对原始金相组织图像进行预处理,建立金相组织图像数据集。针对建立的铁素体和马氏体金相组织图像数据集,提出了适合金相组织图像辨识的改进模型,分别记为LeNet-MetStr模型、AlexNet-MetStr模型和VGGNet-MetStr模型。对3种改进卷积神经网络进行模型训练及分析,结果表明VGGNet-MetStr模型对2种金相组织图像自动辨识具有更高的准确度。

The convolutional neural model was used to automatically identify metallographic structure images to reduce the error rate of manual resolution of metallographic structure image categories and improve the resolution efficiency.Two kinds of metallographic structure images of ferrite and martensite obtained from metallographic sample blocks were analyzed,and a preprocessing scheme conforming to the distribution characteristics of the metallographic structure image was proposed.Image size normalization,gray value normalization,and Gaussian smoothing are used to establish the metallographic image sample set and training set.Aiming at the established image data sets of two types of metallographic structures such as ferrite and martensite,the improved models suitable for metallographic structure image recognition are proposed,which are named the LeNet-MetStr model,AlexNet-MetStr model,and VGGNet-MetStr model,respectively.Three improved convolutional neural networks were trained and analyzed.The results show that the VGGNet-MetStr model has higher accuracy for the automatic identification of two kinds of metallographic structure images.