DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR

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 e...

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Main Author: ISMAIL, NUR HIDAYAH
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.21747 /
Published: IRC 2020
Subjects:
Q Science (General)
Online Access: http://utpedia.utp.edu.my/21747/1/17007440_Nur%20Hidayah%20binti%20Ismail.pdf
http://utpedia.utp.edu.my/21747/
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spelling utp-utpedia.217472021-09-23T23:39:52Z http://utpedia.utp.edu.my/21747/ DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR ISMAIL, NUR HIDAYAH Q Science (General) 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. IRC 2020-09 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/21747/1/17007440_Nur%20Hidayah%20binti%20Ismail.pdf ISMAIL, NUR HIDAYAH (2020) DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR. IRC, Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
collection UTPedia
language English
topic Q Science (General)
spellingShingle Q Science (General)
ISMAIL, NUR HIDAYAH
DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR
description 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.
format Final Year Project
author ISMAIL, NUR HIDAYAH
author_sort ISMAIL, NUR HIDAYAH
title DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR
title_short DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR
title_full DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR
title_fullStr DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR
title_full_unstemmed DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR
title_sort deep learning-based traffic light detector
publisher IRC
publishDate 2020
url http://utpedia.utp.edu.my/21747/1/17007440_Nur%20Hidayah%20binti%20Ismail.pdf
http://utpedia.utp.edu.my/21747/
_version_ 1741195780782292992
score 11.62408

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