摘要
提出了一种基于网中网卷积神经网络对红枣进行缺陷检测的方法,在原有AlexNet卷积神经网络的基础上增加了1×1隐含感知层,增强了网络的非线性以提取更抽象的特征;并采用全局平均池化层的方式替换全连接层,减少大量参数的同时提升了识别准确率。对新疆骏枣进行了实测,可将红枣分为好枣、黑斑枣、皱枣、叠枣、脱皮枣、黄皮枣和裂枣7类,表明该方法与基于常规SVM的视觉检测方法和基于AlexNet网络的分类方法相比,分类效果得到了有效提升。
We proposed a method of defect detection for jujube based on a neural network with network-in-network convolutional(NIN-CNN).This method adds 1×1 hidden perception layer to the original AlexNet convolution neural network;enhances the non-linearity of the network to extract more abstract features;and replaces the fully connected layer with the global average pooling layer,which improves the recognition accuracy while reducing a large number of parameters.In this study,Jun jujube in Xinjiang is tested.The jujube is divided into seven categories,including healthy jujube,black-spotted,wrinkled,overlapping,peeling,yellow-skinned and crack.The experimental results show that the classification effect of the proposed method is improved effectively, compared with the conventional visual detection method with SVM and the classification method with AlexNet network.
作者
杨志锐
郑宏
郭中原
许晓航
YANG Zhi-rui;ZHENG Hong;GUO Zhong-yuan;XU Xiao-hang(School of Electronic Information,Wuhan University,Wuhan,Hubei 430072,China)
出处
《食品与机械》
北大核心
2020年第2期140-145,181,共7页
Food and Machinery
关键词
红枣
缺陷检测
网中网卷积神经网络
jujubes
defect detection
neural network with network-in-network convolution
