Road Sign Detection Using YOLO’s Latest Releases: An Evaluation Study of v8, v10, and v11

This paper presents a
comparative analysis of three recent YOLO variants—YOLOv8,
YOLOv10, and YOLOv11—evaluated on a traffic sign detection
task under variable real-world visual conditions. The nano
variant of each model was evaluated in terms of precision, recall,
mean average precision (mAP), training efficiency, F1-confidence,
and runtime speed. This study offers practical insights for
deploying object detection models in intelligent transportation systems, aiming to balance real-time performance with detection accuracy. The results indicated that YOLOv8 achieved the highest mAP (0.92), followed by YOLOv11 (0.908) and YOLOv10 (0.873). In terms of runtime performance, YOLOv8 and YOLOv11 demonstrated comparable speeds on the test data, whereas YOLOv10 required more time to complete the inference process