Traffic Light Detection and Recognition using Ensemble Learning with Color-Based Data Augmentation

With the advances of deep neural networks, there is progress on the detection and recognition of traffic lights for advanced driver assistance systems (ADAS). However, existing approaches most rely on the identification of traffic light boxes, followed by the recognition of signal lights. It is considered as a major drawback since light bulbs can be arranged in different directions or irregular patterns in different geographic regions. In this paper, we present a traffic light detection method based on direct recognition of individual signal lights. Our two-stage technique utilizes data augmentation and ensemble learning to detect the light bulbs with least miss rate. By learning the color characteristics from validation sets for data augmentation, it is able to achieve a signal light candidate detection rate at 97.26%. Followed by the classification stage, the recognition accuracy is given by 98.6%, which outperforms state-of-the-art traffic light detection algorithms. The source code and dataset are available at https://github.com/981124/yolov7 traffic light detect.