Firstly

the network model is regularized to improve the generalization ability network and sparsity of model. Then

the combined pruning algorithm is adopted. Convolutional layer convolution kernel is firstly deleted

and then the full connection layer weight is deleted. The pruning process is carried out several times

and each time after pruning

it is retrained to restore the model classification and recognition performance. The experimental results show that the combined pruning algorithm can compress the network model by 79 % and accelerate by 3. 38 times while ensuring that the VGG-16 network keeps the classification and recognition rate of the vehicle dataset at92. 84 %.