Research on Traffic Police Gesture Recognition Algorithm Based on Improved YOLOv11

Traffic police gesture recognition is essential for intelligent traffic management, road safety, and autonomous driving. This paper proposes a novel recognition approach based on an improved YOLOv11 architecture to address challenges such as diverse gesture categories, large scale variations, and environmental interference. We propose a novel Global-Local Pooling Fusion Block to reduce model complexity while maintaining feature quality, introduce a Global Context (GC) attention mechanism to enhance focus on key gesture regions, and integrate an improved Lite-BiFPN structure for better multi-scale feature fusion. Experimental results show that our method achieves a mean average precision of 90.6%, which is 3.8% higher than the original YOLOv11. This work significantly improves detection accuracy, robustness, and real-time performance, providing strong support for intelligent driving systems under complex traffic conditions.