Open AccessTraffic Sign Classification Using CNN
Author(s) -
Kolachalama Venkata Naga Sreya
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.37700
Subject(s) - convolutional neural network , traffic sign recognition , computer science , grayscale , sign (mathematics) , artificial intelligence , feature (linguistics) , field (mathematics) , scope (computer science) , pattern recognition (psychology) , artificial neural network , image (mathematics) , data mining , traffic sign , mathematics , mathematical analysis , linguistics , philosophy , pure mathematics , programming language
With the increasing necessity of autonomous electric vehicles as time passes by, there are a lot of technical prospects within the structure that have a lot of scope for advancement. One such prospect is traffic sign recognition. Several models have already been developed and are in practice but it is evident to everyone within this field that there is still a lot of untapped potential. In this project we implement feature classification using convolutional neural networks to achieve an efficiency and accuracy higher than that of a conventional model. The system first converts the image into grayscale and then three layers of the image are created. By using skilled convolutional neural network which incorporates crucial data of traffic signs and images, they are parallelly assigned to corresponding classes. Results have shown that this system works with great efficiency. Index Terms: Traffic sign, GTSRB, ALVINN, Neural Network, CNN, LeNet, ReLU, Evaluation, Epoch