Paper Contents
Abstract
For the development of intelligent system detection and recognition of traffic sign is very crucial. We proposed an algorithm for enhancing the traffic sign detection and recognition that address the problems such as such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. In this paper, we use convolutional neural network and pickle file model for the recognition of traffic sign. For the analysis of image dataset, it is taken from the GSTRB (German Traffic Sign Recognition Benchmark) which comprises 51,839 images and it is divided into training and testing sets. The experimental results of proposed methodology generate the accuracy about 99% for each traffic sign, which is much better than the existing traffic sign recognition system. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.
Copyright
Copyright © 2023 Gyana Chopra. This is an open access article distributed under the Creative Commons Attribution License.
