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DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR

ISMAIL, NUR HIDAYAH (2020) DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR. IRC, Universiti Teknologi PETRONAS. (Submitted)

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Abstract

Nowadays, there are still drivers who habitually do not follow the traffic light rule; they do not stop at the red light. This dissertation presents a Deep Learning-based Traffic Light Detector. The proposed model performs traffic light detection using a Convolutional Neural Network (CNN) to extract specific color features. CNN consists of 6(six) Convolutional layers. It is a fully connected layer that takes the convolution or pooling output and determines the appropriate mark to identify the image. A survey has been carried out gauging the proposal's market potential; 37 respondents stated a need for a traffic light alert system. The system is developed on an Asus VivoBook 15 laptop. A webcam is used to capture the traffic light image. The output is in the form of audio that alerts the red color traffic light.

Item Type: Final Year Project
Academic Subject : Academic Department - Information Communication Technology
Subject: Q Science > Q Science (General)
Divisions: Sciences and Information Technology > Computer and Information Sciences
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 23 Sep 2021 23:39
Last Modified: 23 Sep 2021 23:39
URI: http://utpedia.utp.edu.my/id/eprint/21747

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