Detection of Traffic Sign Using CNN

Abstract

For past more than half of the decade, the detection and recognition of the traffic signs are an active research area; especially in the field of automation of driving that is critical for driverless driving, as there is drastic increase in road accidents due to ignorance of traffic signs and rules. This is frequently used for recognizing permanent or temporary various road signs which are displayed on the side of every small and long road. A complete recognition system may consist of detection of traffic signs as well as their recognition. The detection of traffic sign as well as their recognition is typically used on portable devices. This helps the devices to make better decisions and improve their driving algorithms. The preferable parameters that need to be considered are size of the traffic sign boards and speed of the vehicles. This study addresses the real time detection and recognition of traffic sign from the traffic sign boards through convolutional neural networks. Furthermore, image segmentation techniques such as edge-based segmentation, region-based segmentation, cluster-based segmentation, and so on, play an important role in traffic sign detection and recognition. Also, convolutional neural networks lead to a robust system that achieves higher accuracy in detection phase as well as higher performance ability in training and recognition phase. The proposed model achieves recognition accuracy up to 98.13% at the training.