ScholarWorks@성균관대학교

초록

Reliable detection of traffic signs plays a critical role in autonomous vehicles and driver-assistance technologies, especially when operating under diverse and complex real-world conditions. This study investigates the effectiveness of various YOLO-based object detection frameworks, ranging from YOLOv5 to the newly proposed YOLOv11, in addressing the task of traffic sign recognition. The dataset includes varying class configurations and challenging conditions such as low-light and noise, enabling a comprehensive analysis of model robustness. YOLOv11 outperformed prior versions, achieving the highest mAP of 0.961, establishing it as a state-of-the-art (SOTA) solution. The study also conducted ablations to analyze the impact of batch size, data augmentation, and adverse conditions on model performance. Notably, YOLOv11 demonstrated strong resilience to noise and low-light scenarios, maintaining high accuracy even under dense conditions. Comparisons with existing models further validated its superiority in terms of precision and consistency. These results highlight YOLOv11's potential for real-world traffic sign detection tasks, where reliability is critical. © 2025 IEEE.

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