DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR - ISMAIL, NUR HIDAYAH (IRC, 2020)
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.