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 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.