Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156580
Title: Traffic sign classification with deep learning
Authors: Lee, Ray Sheng
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Lee, R. S. (2022). Traffic sign classification with deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156580
Project: SCSE21-0252
Abstract: With the sudden surge in Electric Vehicle (EV) stocks in the stock market, the author has been particularly interested in the development of these EVs and their technologies. In this project, the author aims to explore traffic sign classification in the local context using existing classification methods. The traffic signs are essential for accident-free and quick driving. When traffic signs are recognised by automated systems that are accurate and quick, it gives drivers an advantage in navigating. As a result, automatic traffic sign identification is critical, especially in intelligent transportation systems. The automated recognition system gathers essential data regarding traffic signs, assists the driver in making timely decisions, and improves driving safety and comfort. This paper provides an overview on the development of deep learning technologies, specifically using Convolutional Neural Networks alongside Keras to classify traffic signs in Singapore and Germany.
URI: https://hdl.handle.net/10356/156580
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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