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Real Time Traffic Sign Recognition using Deep Learning

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dc.contributor.authorSarmindaran Naidu a/l Siva Kumaren_US
dc.date.accessioned2023-05-03T17:39:26Z
dc.date.available2023-05-03T17:39:26Z
dc.date.issued2020-02
dc.descriptionFYP 2 SEM 2 2019/2020en_US
dc.description.abstractA traffic sign detection and recognition system (TSDR) is a necessity for the development of advanced driver assistance systems (ADAS). The traffic recognition system guides the driver to make correct decisions for safe driving. Such a system in Malaysia helps to reduce road accidents in the country that happens due to carelessness. Research community shows a significant interest in vision-based TSDR studies which are mainly motivated by three factors, which are detection, tracking and classification. The image recognition methods using deep learning are far superior to the methods used before the appearance of deep learning in general object recognition field. A real-time detection, tracking and classification framework to classify Malaysian traffic signs in real-time is proposed in this study. Hence, this paper explains how deep learning is applied to the Malaysian traffic sign recognition model using Yolov3 detection algorithm. This document presents an understanding, analysis on findings and research in the development of traffic sign recognition model.en_US
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21682
dc.language.isoenen_US
dc.subjectreal-timeen_US
dc.subjectrecognitionen_US
dc.subjectdeep learningen_US
dc.titleReal Time Traffic Sign Recognition using Deep Learningen_US
dspace.entity.typePublication
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