图像模式识别, 双路卷积神经网络, 小尺度复杂图像, 神经网络可视化

In order to improve the recognition accuracy of small-scale complex images, an edge channel is added into LeNet-5 convolutional neural network to process the edge information. By combining the different features generated by two channels to construct a classifier, a 2-channel convolutional neural network is proposed to identify small-scale complex data sets. Classification results on ten types of product data show that the accuracy of the 2-channel convolutional neural network is much higher than that of the traditional network. Finally, the neural network visualization algorithm is adopted to visualize and analyze the 2-channel convolutional neural network.