A practical approach for detection and classification of traffic signs using Convolutional Neural Networks

Automatic detection and classification of traffic signs is an important task in smart and autonomous cars. Convolutional Neural Networks has shown a great success in classification of traffic signs and they have surpassed human performance on a challenging dataset called the German Traffic Sign Benchmark. However, these ConvNets suffer from two important issues. They are not computationally suitable for real-time applications in practice. Moreover, they cannot be used for detecting traffic signs for the same reason. In this paper, we propose a lightweight and accurate ConvNet for detecting traffic signs and explain how to implement the sliding window technique within the ConvNet using dilated convolutions. Then, we further optimize our previously proposed real-time ConvNet for the task of traffic sign classification and make it faster and more accurate. Our experiments on the German Traffic Sign Benchmark datasets show that the detection ConvNet locates the traffic signs with average precision equal to 99.89%. Using our sliding window implementation, it is possible to process 37.72 high-resolution images per second in a multi-scale fashion and locate traffic signs. Moreover, single ConvNet proposed for the task of classification is able to classify 99.55% of the test samples, correctly. Finally, our stability analysis reveals that the ConvNet is tolerant against Gaussian noise when σ<10.