Aiming at the problem that traffic sign images are susceptible to complex background

illumination and motion blur

resulting in low recognition rate and slow recognition speed

a traffic sign recognition method based on asymmetric two-channel convolutional neural network is proposed.Rich feature information is extracted through two paths of different network structures.The upper layer path uses shallow local features and deep layer global features extracted by hop layer connection and the fine features extracted from the lower layer path are fused in fully connected layer

and the activation function LReLUs is used instead of fragile ReLU

which improves accuracy.The experimental results are compared with other algorithms

it is proved that the recognition rate and recognition speed of the proposed algorithm are prior to other algorithms

which has certain advancement and robustness.