This study evaluates YOLOv12 using a globally sourced traffic dataset that includes varied weather conditions, lighting scenarios, and geographic locations. The model demonstrates strong performance ...

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 ...

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 ...

Module for detecting traffic lights in the CARLA autonomous driving simulator. Based on the YOLO v2 deep learning object detection model and implemented in keras, using the tensorflow backend ...

The Traffic Light Detection and Classification project aims to enhance autonomous driving systems by accurately detecting and classifying traffic lights. The model is designed to generate appropriate ...

Our algorithm considers consecutive detection of the same light and uses Kalman filtering techniques to provide each target’s smoother and more precise position. Our pipeline has been validated for ...