Date of Award

5-2022

Access Restriction

Campus Access only Research Projects

Degree Name

Master of Science

Department

Computer Science

School or College

Seaver College of Science and Engineering

First Advisor

Robert Johnson

Abstract

Traffic sign recognition systems have been introduced to overcome road-safety concerns. These systems are widely adopted by automotive industry whereby safety critical systems are developed for car manufacturers. To develop an automatic TSDR system is a tedious job given the continuous changes in the environment and lighting conditions. Among the other issues that also need to be addressed are partial obscuring, multiple traffic signs appearing at a single time, and blurring and fading of traffic signs, which can also create problem for the detection purpose . For applying the TSDR system in real-time environment, a fast algorithm is needed. As well as dealing with these issues, a recognition system should also avoid erroneous recognition of no signs. TSDR system would detect and classify a collection of 43 individual traffic-signs taken from real-time environment into different classes for recognition. In this project classification of individual traffic signs is done using deep Convolutional Neural Network with VGG-net architecture model to develop an efficient classifier with improved prediction accuracy (using GTSRB dataset).

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