Traffic sign recognition method based on dual channel CNN
Aiming at the problem of traffic sign recognition,the traditional LeNet-5 network structure has low accuracy and slow recognition,and ignores the influence of natural factors such as weather.Through convolution neural network technology,an improved two channel and multi-scale network structure model based on LeNet-5 is proposed.In the dual channel structure,each channel contains two branch structures,and the number of convolutions of each channel is different from the image scale.Through the feature extraction of images with different scales,the image features become richer.Secondly,the improved network structure greatly increases the number of convolution cores.Finally,change the Sigmoid activation function to Relu activation function,change the random gradient descent algorithm to Adam algorithm,and add dropout layer to prevent over fitting,so as to improve the traffic sign recognition rate.The recognition rate of the improved network is 98.6%,floating up and down by 0.5%.Compared with the traditional LeNet-5 network structure,the recognition rate is increased by more than 15%.It is verified that the improved network structure has certain robustness.
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