Fine Tuned YOLOv11-Based Road Sign Detection

Résumé

The rapid growth of autonomous driving and intelligent transportation systems has increased the need for accurate and efficient traffic sign detection. Recognizing traffic signs in real time plays a crucial role in enabling vehicles to understand and respond to dynamic road conditions, ensuring both safety and regulatory compliance. This study investigates the performance of three lightweight YOLOv11 variants, YOLOv11n, YOLOv11s, and YOLOv11m, for road sign detection, aiming to balance accuracy with computational efficiency for real-time deployment in resource-constrained environments. Each model was trained and evaluated using a consistent traffic sign dataset, with performance metrics including precision, recall, mean Average Precision (mAP), and F1 score. The YOLOv11n model demonstrated stable training behavior and achieved a peak mAP@0.5 of 0.52, with a mean F1 score of 0.47, indicating efficient detection of dominant classes but limited performance for underrepresented ones. The YOLOv11s showed improved generalization and localization abilities with a higher mAP@0.5 of 0.55 and a mean F1 score of 0.64, suggesting a balanced trade-off between speed and accuracy. The most advanced variant, YOLOv11m, achieved the highest mAP@0.5 of 0.70 and an F1 score of 0.63, demonstrating robust detection and convergence properties. However, all models exhibited difficulty in detecting rarely represented classes, such as "crosswalk," emphasizing the importance of dataset balancing. These findings confirm the suitability of these YOLOv11 variants for embedded traffic monitoring systems and highlight avenues for further improvement through data augmentation and fine-tuning.

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