Traffic Light Detection and Recognition Using a Two-Stage Framework From Individual Signal Bulb Identification

This research addresses the critical issue of traffic light detection and recognition in advanced driver assistance systems. In this paper, we propose a novel two-stage detection framework which is capable of recognizing different types of traffic lights, including arrow signals. Unlike most existing methods which detect entire traffic light boxes, our focus is on individual signal bulbs for detection and recognition. In the first stage, our approach aims to achieve an exceptionally low miss rate in detecting traffic signal bulbs, even the result contains many false positives. In the second stage, a classification network is employed to identify the correct traffic signal lights. Our method effectively tackles the diverse traffic light detection problems posed by varying arrangements of signal bulbs in different regions. Moreover, it can simultaneously detect individual traffic signals including various types of arrow lights. The experiments with real road scene images and public datasets have demonstrated the feasibility and effectiveness of our proposed technique.