Enhanced YOLOR for Accurate and Real-Time Traffic Sign Detection in Autonomous Driving

Résumé

Traffic sign detection systems are vital for improving road safety and supporting autonomous navigation in urban environments. This paper presents a fine-tuned traffic sign detection system based on the You Only Learn One Representation (YOLOR) architecture. The model was trained and evaluated on a dataset comprising 15 traffic sign classes: Green Light, Red Light, Speed Limit 10, Speed Limit 100, Speed Limit 110, Speed Limit 120, Speed Limit 20, Speed Limit 30, Speed Limit 40, Speed Limit 50, Speed Limit 60, Speed Limit 70, Speed Limit 80, Speed Limit 90, and Stop. To enhance detection performance across diverse classes, the model was fine-tuned to accurately detect and classify these elements under varying conditions. The experimental results demonstrate strong detection capabilities, achieving a precision of 87.5%, a recall of 87.8%, a mean Average Precision at IoU 0.5 (mAP@0.5) of 88.5%, and a mAP across IoU thresholds from 0.5 to 0.95 (mAP@0.5:0.95) of 77.7%. These results highlight the effectiveness of the YOLOR-based approach for real-world traffic sign recognition tasks, offering a promising solution for intelligent transportation and autonomous driving applications. Furthermore, the model's competitive performance compared to recent methods reinforces its relevance in current state-of-the-art benchmarks.

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