A Study on the Lightweighting of YOLOv11-based Traffic Sign Detection Models

Accurate and real-time traffic sign detection is a critical component of environmental perception for Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles, playing a vital role in ensuring driving safety. However, current mainstream lightweight object detection models often encounter performance bottlenecks when dealing with challenges prevalent in real-world road scenes, such as small objects and complex backgrounds, primarily due to the loss of feature information. To address this issue, this paper conducts an in-depth application and performance evaluation of an improved YOLOv11-based lightweight detection algorithm. The algorithm is characterized by two key features: first, it enhances the perceptual capabilities for tiny objects by introducing a higher-resolution P2 feature layer; second, it adopts an integrated fusion detection module, termed FPN-P2 in this paper, which applies a "shallow feature direct-path, deep feature fusion" strategy to efficiently integrate multi-scale features. Extensive experiments on the public TT100K dataset demonstrate that the optimized configurations of this architecture achieve significant breakthroughs in both performance and efficiency. Specifically, its performance-optimal configuration achieves a mAP@0.5 of 76.3%, substantially surpassing baseline models like YOLOv8n. Meanwhile, its efficiency-optimal configuration attains a high accuracy of 68.8% while having fewer parameters than the baseline model. This research not only provides and validates highly competitive solutions for high-precision lightweight traffic sign detection but also, through intensive ablation studies, offers valuable empirical insights into model architecture selection and optimization for specific tasks.