Research on US Traffic Signs Detection Algorithm with YOLO v8

Abstract

Accurate detection of traffic signs, including traffic lights, is essential for autonomous driving systems to ensure safe navigation and adherence to traffic regulations. Traffic signs provide crucial information, such as speed limits, stop signals, lane changes, pedestrian crossings, and warning signs, all of which self-driving vehicles must interpret in real-time to make informed driving decisions. Failure to recognize or misinterpret traffic signs can lead to critical safety risks, making robust detection algorithms a fundamental component of autonomous vehicle technology. This research explores the application of the YOLOv8 algorithm, a state-of-the-art deep learning-based object detection model, to improve the recognition and classification of US traffic signs. By integrating the YOLOv8 algorithm with a well-curated Kaggle dataset, this study aims to enhance the precision and efficiency of traffic sign detection. The findings of this research are expected to contribute to the advancement of autonomous vehicle perception, improving their ability to navigate complex road environments safely and effectively. This study not only enhances traffic sign recognition but also provides insights into optimizing deep learning models for real-time deployment in self-driving applications.