ISMAIL, NUR HIDAYAH (2020) DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR. [Final Year Project] (Submitted)
17007440_Nur Hidayah binti Ismail.pdf
Restricted to Registered users only
Download (1MB)
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 |
---|---|
Subjects: | Q Science > Q Science (General) |
Departments / MOR / COE: | Sciences and Information Technology > Computer and Information Sciences |
Depositing User: | Mr 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 |