A fusion-based approach of deep learning and edge-cutting algorithms for identification and color recognition of traffic lights

Abstract

The detection and color recognition of traffic lights should be the foundation for the capture of illegal driving practices. However, it may be difficult to recognize lights with different colors in intricate and unpredictable surroundings. This study implements a traffic light detection and recognition scheme that can be used for intelligent traffic. First, the images obtained from the speed camera should be pre-segmented. Then the traffic lights with colors are detected by the YOLOv5 model trained based on the image-enhancement dataset. Next, the candidate boxes of traffic lights are edge detected and clipped out of multiple lamp panels in missing video frames. Finally, the color of the candidate boxes will be determined by the lamp panel with the greatest number of bright pixels. This finding shows that the fusion-based approach performs better than a single-based algorithm for identification and color recognition of traffic lights under varying illumination and weather circumstances.