Build a Real-Time Traffic Light Detection System
Blog post from Roboflow
Traffic lights play a crucial role in autonomous driving systems, and their accurate detection is vital for navigation and safety in urban environments. This text outlines a tutorial for creating a real-time traffic light detection system using Roboflow's platform and RF-DETR models. The process begins with training the model using a diverse public dataset from Roboflow Universe, ensuring it includes various urban scenes under different conditions to enhance generalization. The RF-DETR Small model is chosen for its balance between performance and efficiency, achieving an 84.3% mAP@50 and demonstrating strong detection fundamentals despite challenges like glare and motion blur. After training, the model is deployed using Roboflow Workflows, a visual pipeline builder that allows for the integration of detection, visualization, and deployment without the need for infrastructure code. The workflow processes images in real time, displaying annotated outputs, and can be expanded for further applications such as traffic monitoring. While the project showcases the ease of using Roboflow's platform to develop an end-to-end computer vision solution, enhancements like larger datasets and more varied environmental coverage are necessary for deployment-grade systems.