Sign Detection

Sign detection, primarily focusing on traffic signs for autonomous driving and advanced driver-assistance systems, aims to develop accurate and real-time systems for identifying and classifying various signs under diverse conditions. Current research heavily utilizes convolutional neural networks (CNNs), particularly YOLO-based architectures, exploring improvements through transfer learning, adaptive feature pyramids, and novel loss functions to enhance accuracy and robustness, especially for multi-scale objects and under adversarial conditions. These advancements are crucial for improving road safety and enabling more reliable autonomous vehicle navigation.