Traffic Sign Detection

Traffic sign detection aims to automatically identify and classify traffic signs in images or video, crucial for autonomous driving and advanced driver-assistance systems. Current research focuses on improving the accuracy and efficiency of detection, particularly for small or distant signs and under challenging conditions (e.g., poor weather, low light), employing architectures like YOLO, Vision Transformers, and Convolutional Neural Networks often enhanced with attention mechanisms and feature pyramids. These advancements contribute to safer roads by improving driver awareness and enabling more reliable autonomous navigation, with ongoing work emphasizing robustness, real-time performance, and explainability of the detection process.