基于改进的卷积神经网络的钢号识别

为获取样本的多样性特征，提出了一种改进的卷积神经网络结构。该网络中引入多层递归神经网络，利用卷积神经网络提取输入图像的浅层特征，同时利用卷积神经网络和递归神经网络并行提取高层特征，最后将两种网络学习到的特征进行融合输入到分类器中分类。利用迁移学习理论解决小样本集数据训练不足的问题，并将这种卷积神经网络结构应用于石油物资管线钢号识别中。实验探究了递归神经网络个数与卷积核个数对网络性能的影响，实验结果表明，改进的网络结构与其它网络进行对比，错误率降低了 3% 。

Steel Grade Identification Based on Improved Convolution Neural Network

To extract diversity image features，an improved convolution neural network structure is proposed．The network introduced a multi-layer recursive neural network． Firstly，the network learned the shallow features of input images with convolution neural network，and then learned high-level features through the convolution neural network and multi-layer recursive neural network at the same time． Finally input two kinds of high-level features fusion into the classifier． This paper used the theory of transfer learning to solve the problem of the small sample training set data，and applied the improved convolution neural network structure to the oil pipeline steel grade identification． The experiments explore the influence of recursive neural network number and convolution kernels number separately，and make sure the final structure． The results show that the improved convolution neural network gets a low error rate of 3% ．