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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2020
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| 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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my-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) |
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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_facet |
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_ |
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