Recognizing Traffic Signs Using a Practical Deep Neural Network

Abstract

Convolutional Neural Networks (CNNs) surpassed the human performance on the German Traffic Sign Benchmark competition. Both the winner and the runner-up teams trained CNNs to recognize 43 traffic signs. However, both networks are not computationally efficient since they have many free parameters and they use highly computational activation functions. In this paper, we propose a new architecture that reduces the number of the parameters $27\%$ and $22\%$ compared with the two networks. Furthermore, our network uses Leaky Rectified Linear Units (Leaky ReLU) activation function. Compared with 10 multiplications in the hyperbolic tangent and rectified sigmoid activation functions utilized in the two networks, Leaky ReLU needs only one multiplication which makes it computationally much more efficient than the two other functions. Our experiments on the German Traffic Sign Benchmark dataset shows $0.6\%$ improvement on the best reported classification accuracy while it reduces the overall number of parameters and the number of multiplications $85\%$ and $88\%$, respectively, compared with the winner network in the competition. Finally, we inspect the behaviour of the network by visualizing the classification score as a function of partial occlusion. The visualization shows that our CNN learns the pictograph of the signs and it ignores the shape and color information.