DEEP LEARNING-BASED TRAFFIC SIGN OPTICAL RECOGNITION FOR INTELLIGENT TRANSPORTATION

This paper aims to develop a high-efficiency, high-accuracy driver assistance system by integrating deep learning with an optimized Kalman filter approach.The system is designed to recognize traffic signs in complex road environments, enabling the rapid and accurate identification of critical signage to assist drivers in making correct decisions.This paper addresses key challenges in the field of intelligent transportation: in dynamic traffic environments, conventional object detection algorithms struggle to capture deformation features of traffic signs caused by viewpoint variations, resulting in high miss rates for small and deformed targets; existing tracking systems suffer frequent identity switches in densely populated vehicle scenes due to occlusion, compromising tracking continuity; and complex road conditions significantly degrade recognition robustness.To overcome these limitations, the proposed system integrates an enhanced YOLOv11 object detection framework with a Kalman filter-based multi-object tracking algorithm, forming a real-time, end-to-end processing pipeline.Compared to existing technologies, the proposed approach incorporates a deformable convolutional network to enhance spatial feature deformation modeling.The optimized algorithm combines motion trajectory prediction with appearance feature fusion to reduce the frequency of identity switches and mitigate target loss.The mean average precision value has increased; specifically, with 42 categories, the mean average precision of 50 has improved to 0.9222, and with a mean average precision of 50-95, it can also reach 0.7649.