Jingyi Tang

Traffic safety is a significant global concern, and traffic sign recognition (TSR) is essential for the advancement of intelligent transportation systems. Traditional YOLO11s-based methods often struggle to balance detection accuracy and processing speed, particularly in the context of small traffic signs within complex environments. To address these challenges, this study presents Ghost-YOLO-GBH, an innovative lightweight model that incorporates three key enhancements: (1) the integration of a GhostNet backbone, which substitutes the conventional YOLO11s architecture and utilizes Ghost modules to exploit feature redundancy, resulting in a 40.6% reduction in computational load while ensuring effective feature extraction for small targets; (2) the development of a HybridFocus module that combines large separable kernel attention with multi-scale pooling, effectively minimizing background interference and improving contextual feature aggregation by 4.3% in isolated tests; and (3) the implementation of a Bidirectional Dynamic Multi-Scale Feature Pyramid Network (BiDMS-FPN) that allows for bidirectional cross-stage feature fusion, significantly enhancing the accuracy of small target detection. Experimental results on the TT100K dataset indicate that Ghost-YOLO-GBH achieves an impressive 81.10% mean Average Precision (mAP) at a threshold of 0.5, along with an 11.7% increase in processing speed (45 FPS) and an 18.2% reduction in model parameters (7.74 M) compared to the baseline YOLO11s. Overall, Ghost-YOLO-GBH effectively balances accuracy, efficiency, and lightweight deployment, demonstrating superior performance in real-world applications characterized by small signs and cluttered backgrounds. This research provides a novel framework for resource-constrained TSR applications, contributing to the evolution of intelligent transportation systems.