Lightweight Convolutional Neural Networks for Vehicle Target Recognition

In order to improve the network learning effect, based on the existing target learning framework MobileNet-V2, an ELU function is inserted as an activation function. Firstly, use the expansion convolution to increase the number of channels to get more features to activate and output through the ELU function, which can alleviate the disappearance of the gradient of the linear part, the nonlinear part is more robust to the noise of the input change. Then, the way of the residual connection combine high-level features with low-level features and then output. Finally, output to softmax using global pooling. The experimental data shows that compared with the current mainstream lightweight deep learning target recognition algorithm,E-MobileNet has improved the accuracy of recognition and the frame rate per second in the same test environment of the same test set. The experimental data fully demonstrates that the use of the ELU activation function and the global pooling layer reduces the number of parameters, enhances the generalization ability of the model, and improves the robustness of the algorithm. On the basis of ensuring the light weight of the neural network model, the recognition accuracy of the target is effectively improved.