International Conference on Computer and Knowledge Engineering

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A Robust Network for Embedded Traffic Sign Recognation.

Authors :

Omid Nejati Manzari¹ Shahriar Baradaran Shokouhi²

1- School of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13144, Iran 2- School of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13144, Iran

Keywords :

deep neural network, traffic sign recognition, auto-driving, embedded

Abstract :

Traffic sign recognition systems are a key component in real-world applications such as auto-driving and safety and driver assistance. While deep neural networks in recent years have achieved high accuracy in the classification of these traffic signs, there is always the discussion of the high computations of these networks and their many teachable parameters. A significant challenge is to design a compact deep neural network for the application of traffic sign recognition. This paper proposes a network that uses residual blocks in the network to obtain a top-1 accuracy of 99.51 for the German traffic sign recognition benchmark, while the number of parameters is ∼430,000, which is ∼32x fewer than the state-of-the-art. Experiments have been performed to show the network's resistance to destructive factors and its comprehensiveness in the application of traffic sign recognition. The results of these tests show that it is a comprehensive and robust network for the recognition of traffic signs.