卷积神经网络(CNN)的结构与参数决定了其在图像分类中的性能,针对深度网络结构复杂、参数量较大的问题,提出了一种基于稠密连接网络进化的CNN(D-ECNN)图像分类算法。该算法可对网络结构空间进行有效搜索,并基于有限的计算资源对深度网络结构与参数进行自适应优化。在车辆数据集上的分类实验结果表明,本算法的准确率可达到95%,相比视觉几何组(VGG16)算法,提升了约1%,且本算法的模型文件较小、速度更快。

The structure and parameters of convolutional neural network (CNN) determines the performance of image classification. Aiming at the problems of complex structure and a parameters that require a lot of manual settings in deep network, a CNN image classification algorithm based on the evolution of densely connected networks(D-ECNN) is proposed in this work. The algorithm can effectively search the network structure space, and realizes the adaptive optimization of deep network structure and parameters based on limited computing resources. The classification experiment results on the vehicle data set show that the accuracy of this algorithm can reach more than 95%, which is about 1% higher than that of the visual geometry group (VGG16) algorithm. The model file of this algorithm is smaller and the test speed is faster.