Traffic Signs Recognition Using CNN

Convolutional Neural Networks (CNN) are already being used to perform an increasing number of object identification challenges. Most of the existing and new computer vision tasks has been improved by CNNs high recognition rate and execution. In this work a convolution neural network is employed to develop a traffic sign recognition system. In addition, the research compares and contrasts numerous CNN architectures. The neural network's training is implemented using Tensor flow and Keras library. The proposed model has accuracy of 93.58.