YOLO-Based Traffic Sign Detection for Autonomous Vehicles

YOLO-Based Traffic Sign Detection for Autonomous Vehicles

Nilesh Sham Sonawane1, Dipali Sanjay Porje2

1Assistant Professor, Department of Artificial Intelligence And Data Science, Guru Gobind Singh College of Engineering And Research Centre

2Student, Department of Artificial Intelligence And Data Science, Guru Gobind Singh College of Engineering And Research Centre

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Abstract - Real-time traffic sign recognition is an essential part of intelligent transportation systems and autonomous vehicles. This paper describes a method involving YOLOv8n, a small object detection model, to conduct high-speed and accurate traffic sign recognition. The approach includes preprocessing, augmentation, and training on a labeled dataset of traffic signs. Our model results in a mean Average Precision (mAP) of 93.5, which beats earlier lightweight models in terms of efficiency. Our proposed method also proves to be resilient in the real world and thus can be deployed in embedded systems. Improved detection under conditions of extreme weather will be dealt with in the future.

Key Words : Traffic Sign Detection, YOLO , Intelligent Transportation System , Computer Vision