Real Time Traffic Light Detection and Classification using Deep Learning
Zakaria Ennahhal, Ismaïl Berrada, Khalid Fardousse · 2019
Traffic light detection and classification represent a major issue for autonomous driving. Although a number of works have been published on this topic, providing a real-time processing solution is still a challenging task. In this paper, we show, by experimenting three models, namely “Faster R-CNN”, “R-FCN” and “SSD” on and two datasets, namely “Bosch Small Traffic Light Dataset” and “Lisa Traffic Light Dataset”, that we can achieve a higher accuracy while reducing the detection and recognition time. In order to improve the overall performance and take the best score of the trained models, we used the ensembling modeling technique. The obtained results outperform the state-of-the-art.