基于卷积神经网络的木材缺陷识别

Wood defect recognition based on convolutional neural network

跨层卷积神经网络模型由输入层、两个交替的卷积层、池化层、全连接层和输出层组成。池化层输出到全连接层,将网络的高层次特征和低层次特征相结合构造分类器。在网络中加入Dropout技术,以防止过拟合的发生。

A cross-layer convolution neural network model is composed of input,two staggered convolution,pooling,full connection and output layers.The output of the two pooling layers are sent to the full connection layer for building the classifier with both high-level features and low-level features of the network.Dropout technology is added to the network for preventing over-fitting.