重组牛肉图像识别模型的比较研究

Comparative Study on Image Recognition Models for Restructured Beef

以重组牛肉为研究对象,基于机器视觉技术构建3种深度残差网络(deep residual network,ResNet)模型(ResNet-50、ResNet-101、ResNet-152)用于识别重组牛肉,同时应用VGG-16视觉几何群网络模型、支持向量机模型以及LeNet-5卷积神经网络模型,比较分析ResNet模型的识别准确率和响应时间。采集并经过图像预处理后共得到6168张样品图像作为实验样本,随机选取其中的4936张作为训练集,剩余1232张作为测试集。结果表明:3种ResNet模型(ResNet-50、ResNet-101、ResNet-152)识别速率较快,准确率高,均可以有效识别重组牛肉,且卷积层越多,准确率越高,其中ResNet-50模型识别准确率达到较高水平,且测试时间仅需0.45 s,能够准确、快速地识别重组牛肉。

Three deep residual network(ResNet)models(ResNet-50,ResNet-101 and ResNet-152)to quickly identify restructured meat were built based on machine vision technology,and they were comparatively analyzed for recognition accuracy and response time applying visual geometry group network(VGG-16)model,support vector machine(SVM)model and LeNet-5 convolution neural network model.Images of restructured beef steak samples were collected and preprocessed.As a result,a total of 6168 images were obtained for this research,4936 of which were randomly selected as the training group,and the remaining 1232 were used as the test group.The results showed that all the three ResNet models could fast and accurately identify restructured beef steak.With more convolution layers,the accuracy was higher.The ResNet-50 model exhibited higher recognition accuracy with testing time of only 0.45 s and it was a better one to accurately and quickly identify recombined ground beef.