Real-Time Traffic Sign Detection System for Autonomous Driving Based on YOLO Algorithm

This research aims to optimize the YOLO algorithm to improve the real-time performance and accuracy of traffic sign detection in autonomous driving systems. By using model pruning and quantization technology, the inference time is significantly reduced, ensuring that the model can still run efficiently on resource-constrained edge devices. Experimental results show that the optimized model shows excellent detection performance in different scenarios of urban roads and highways, especially under complex lighting and high-speed driving conditions. Although the model performance is slightly insufficient under extreme weather and specific sign types, the overall optimization solution greatly improves the real-time performance and robustness of the detection system, providing a more efficient solution for traffic sign detection in autonomous driving.