A Hybrid Approach for Efficient Traffic Sign Detection Using Yolov8 And SAM

In this study, we present an innovative hybrid approach for traffic sign detection in autonomous driving, combining YOLOv8's real-time detection capabilities with the Segment Anything Model (SAM), enhanced through Visual Prompt Tuning. This methodology addresses the challenge of accurately identifying diverse traffic signs, particularly those that are less common or visually complex. After the initial detection by YOLOv8, the SAM (Segment Anything Model), enhanced with Visual Prompt Tuning, is then applied to refine and improve the accuracy of these detections, especially for signs that are less common or have complex visual features. This two-step process, combining the quick detection capabilities of YOLOv8 with the detailed segmentation ability of SAM, aims to achieve high accuracy in traffic sign recognition, which is essential for the safe operation of autonomous vehicles.