Traditional methods in machine learning for detecting traffic lights and classification are replaced by the recent enhancements of deep learning object detection methods by success of building convolutional neural networks (CNN), which is a component of deep learning. This paper presents a deep learning approach for robust detection of traffic light by comparing two object detection models and by evaluating the flexibility of the TensorFlow Object Detection Framework to solve the real-time problems. They include Single Shot Multibox Detector (SSD) MobileNet V2 and Faster-RCNN. Our experimental study shows that Faster-RCNN delivers 97.015%, which outperformed SSD by 38.806% for a model which had been trained using 441 images.