TRAFFIC LIGHT DETECTION WITH YOLO MODELS

Abstract

The accurate detection and recognition of traffic lights are paramount in the realm of autonomous driving systems and intelligent traffic management. This study leverages the comprehensive cinTA_v2 Image Dataset on Robotflow, specifically designed for traffic light detection, to evaluate the performance of advanced You Only Look Once (YOLO) models, including YOLOv7l, YOLOv8n, YOLOv8s, and YOLOv8m. Through meticulous training and evaluation, we systematically analyze the models' ability to accurately detect and classify traffic light states (green, red, and yellow) under a variety of challenging conditions. Our findings reveal significant improvements in precision, recall, and mean Average Precision (mAP) across the models, with YOLOv8m demonstrating superior overall performance, especially in terms of mAP50-95, reflecting its enhanced capability in detecting small and partially obscured traffic lights. The study not only showcases the effectiveness of YOLO models in a critical application within the autonomous driving domain but also highlights the potential for further advancements in traffic light detection technologies. By discussing the challenges, limitations, and future directions, this work contributes to the ongoing efforts to improve road safety and efficiency through the application of cutting-edge artificial intelligence techniques.