基于深度残差网络和GRU的SqueezeNet模型的交通路标识别

Traffic road sign recognition based on SqueezeNetmodel with deep residual network and GRU

Existing traffic road sign recognition methods are all based on convolutional neural networks. As the number of the model network layers increases, the recognition accuracy will also be improved, but there are still some problems such as the reduction of efficiency and the increase of the number of parameters. Therefore, an improved SqueezeNet model combining deep residual network with GRU neural network (SqueezeNet IR GRU) is proposed. In order to enhance the learning efficiency, ELU function is used as the activation function. To avoid the disappearance of gradients when the network layer is too deep, a deep residual network is introduced to guarantee the stability of the model, GRU neural network that can memorize the important past features is utilized. Experiments were performed on the Cafir 10 and GTSRB datasets, and their recognition accuracy rates are above 99.13% and 88.25%respectively. The experimental results show that the SqueezeNet IR GRU model not only reduces the parameter amount greatly, but also its convergence, stability and recall rate are all much better than others.