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...
| Main Author: | ISMAIL, NUR HIDAYAH |
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| Format: | Final Year Project |
| Language: | English |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-utpedia.21747 / |
| Published: |
IRC
2020
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| Subjects: | |
| 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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| Summary: |
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. |
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